CHARACTERIZATION OF THE OFF- ROAD
EQUIPMENT POPULATION
ARB Contract No. 04- 315
Final Report
Prepared for:
California Air Resources Board
and the
California Environmental Protection Agency
Prepared by:
Rick Baker, Principal Investigator
Eastern Research Group, Inc.
December 2008
Disclaimer
The statements and conclusions in this Report are those of the contractor and not necessarily
those of the California Air Resources Board. The mention of commercial products, their source,
or their use in connection with materials reported herein is not to be construed as actual or
implied endorsement of such products.
Acknowledgements
The contributions of the California Air Resources Board staff, particularly Dr. Tao Huai and
Dorothy Shimer, who made invaluable suggestions as Project Officers were greatly appreciated.
We thank the Ag Tech Advisory Committee, including the following individuals: Manuel
Cunha, Jr., Roger Isom, Shirley Batchman, Karla Kay Fullerton, and Cynthia Corey, for their
input and support. We also wish to thank Western Engineering Contractors and CSI
Construction for their cooperation with the instrumentation portion of the study.
We wish to acknowledge the California Cotton Ginners and Growers Associations, the Nisei
Farmers League, the California Grape & Tree Fruit League, the California Citrus Mutual, and the
Fresno County Farm Bureau for encouraging their membership to participate in the survey effort.
The instrumentation portion of the project could not have been completed without the generous
cooperation of the following off- road equipment fleet operators: City of Davis, City of
Woodland, Sacramento County, City of Fresno, City of Clovis, Tiechert Construction, Doug
Veerkamp General Engineering, City of Folsom, Western Engineering, and CSI Construction.
Finally, we thank Scott Rowland and Francine Baker of ARB’s Mobile Source Control Division,
and Michael Benjamin, David Chou, and Debbie Futaba of ARB’s Planning and Technical
Support Division, who were instrumental in reviewing findings, commenting, and providing
supporting data throughout the project.
This Report was submitted in fulfillment of ARB contract number 04- 315, “ Characterization of
the Off- Road Equipment Population,” by Eastern Research Group, Inc., NuStats, LLC, and
SDV- ACCI under the sponsorship of the California Air Resources Board. Work was completed
as of June 17, 2008.
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Table of Contents
Abstract ................................................................................................................... v
Executive Summary ................................................................................................................... 1
1.0 Introduction ................................................................................................................... 3
2.0 Materials and Methods...................................................................................................... 6
2.1 Equipment Characterization Survey ..................................................................... 6
2.1.1 Sample Frame Development..................................................................... 6
2.1.2 Survey and Sample Size Determination ................................................... 9
2.1.3 Survey Instrument Design....................................................................... 12
2.1.4 Updates to Phase I Study Design............................................................ 12
2.2 Equipment Instrumentation................................................................................. 13
2.2.1 Data Logger Characteristics.................................................................... 13
2.2.2 Sensor Installation................................................................................... 14
2.2.3 Logger Installation and Removal Procedures ......................................... 16
2.2.4 Equipment Sample.................................................................................. 16
3.0 Results ................................................................................................................. 23
3.1 Equipment Survey Results.................................................................................. 23
3.1.1 Post- Processing and Quality Assurance.............................................................. 23
3.1.2 Survey Rates ..................................................................................................... 31
3.1.3 Respondent Profiles ............................................................................................ 33
3.1.4 Response Weightings.......................................................................................... 40
3.1.5 Equipment Inventory Findings ........................................................................... 44
3.2 Equipment Instrumentation Results.................................................................... 84
3.2.1 Instrumentation Data Processing ........................................................................ 84
3.2.2 Operation Profiles ............................................................................................... 85
4.0 Analysis and Discussion ................................................................................................. 93
4.1 Statewide Equipment Profile Development........................................................ 93
4.1.1 Identification and Selection of Surrogates .............................................. 93
4.1.2 Statewide Equipment Population Estimates ........................................... 98
4.1.3 Statewide Equipment Activity Profiles................................................. 123
4.1.4 Statewide Equipment HP Profiles......................................................... 126
4.2 Uncertainty Analysis and Confidence Intervals ............................................... 130
4.2.1 Activity Estimates................................................................................. 131
4.2.2 Equipment HP Estimates ...................................................................... 133
4.2.3 Equipment Population Estimates .......................................................... 135
4.3 Preemption Analysis ......................................................................................... 138
4.4 Instrumentation Data......................................................................................... 145
5.0 Summary and Conclusions ........................................................................................... 146
6.0 Recommendations......................................................................................................... 149
References ............................................................................................................... 151
Glossary of Terms, Abbreviations, and Symbols ..................................................................... 152
Appendix A Crop Type Assignments for Agriculture Sector................................................... 153
Appendix B SIC Codes by Survey Sector ................................................................................ 158
Appendix C- Questionnaire Designed for Telephone Administration .................................... 161
Appendix D Logger Installation and Retrieval Procedure........................................................ 171
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Appendix E Public Fleets Contacted for Participation ............................................................. 177
Appendix F Instrumented Vehicle Exhaust Gas Temperature Profiles .................................... 182
List of Figures
Figure 1. Location of Recreational Target Sub- Strata ................................................................... 9
Figure 2. Clēaire Data Logger System ( Source: Clēaire) ............................................................ 14
Figure 3. Hall- Effect Sensor Installed in Bell- Housing of Engine .............................................. 15
Figure 4. Idler Pulley/ Hall- Effect Sensor Assembly .................................................................... 15
Figure 5. Equipment Instrumentation Sites ( www. google. com) .................................................... 18
Figure 6. Calendar Showing Days of Logger Operation ............................................................. 19
Figure 7. Agricultural Sector Population Distribution ( w/ out tractors)*..................................... 45
Figure 8. Construction and Mining Sector Population Distribution ( w/ out Electric
Equipment*).................................................................................................................... . 47
Figure 8. Construction and Mining Sector Population Distribution Continued .......................... 48
Figure 9. Residential Sector Equipment Population Distribution................................................ 49
Figure 9. Residential Sector Equipment Population Distribution Continued.............................. 50
Figure 10. Residual Sector Equipment Population Distribution.................................................. 52
Figure 11. Model Year Distribution – Diesel Agricultural Tractors ........................................... 82
Figure 12. Diesel Agricultural Tractor Hrs/ Yr vs. Age ............................................................... 82
Figure 13. Number of Equipment Pieces vs. Reported Acreage, Non- CAFO/ Dairy
Agricultural Sector Respondents ...................................................................................... 94
Figure 14. Number of Equipment Pieces vs. Reported Acreage, Construction/ Mining Sector
Respondents ...................................................................................................................... 96
Figure 15. Number of Equipment Pieces vs. Reported Acreage, Residual Sector Respondents.. 96
List of Tables
Table 1. Pilot and Full Study Completes By Sample Type and Sub- Strata.................................. 9
Table 2. Estimated Number of Sample Records Needed to Meet Survey Targets ..................... 10
Table 3. Target Construction Equipment Categories for Instrumentation................................. 17
Table 4. Instrumented Equipment Detail ................................................................................... 20
Table 5. Electric Equipment Type Descriptions by Survey Sector ........................................... 24
Table 6. Respondent Equipment Types and Corresponding ARB Equipment Type
Assignments ................................................................................................................. 26
Table 7. Basis and Count of Excluded Records......................................................................... 31
Table 8. Call Summary – Second Round Call- backs................................................................. 31
Table 9. Completed Questionnaires by Sample Type................................................................. 32
Table 10. Final Dispositions for Final Off- road Sample ............................................................ 32
Table 11. Completed Surveys by SSI Crop/ Service Type – Agricultural Sector ...................... 33
Table 12. Completed Surveys by SIC Group – Construction and Mining Sector ..................... 34
Table 13. Completed Surveys by Region – Residential Sector ................................................. 34
Table 14. Completed Surveys by SIC Group – Residual Sector ............................................... 34
Table 15. Completed Agricultural Surveys by Self- Reported Crop Type................................. 35
Table 16. Completed Surveys and Associated Acreage by County – Ag. Sector ..................... 35
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Table 17. Completed Surveys by County – Construction and Mining Sector........................... 37
Table 18. Completed Surveys by County – Residential Sector ................................................. 37
Table 19. Completed Surveys by County – Residual Sector ..................................................... 38
Table 20. Agricultural Respondent Mean Acreage by Crop Type ............................................ 38
Table 21. Agricultural Respondent Pieces of Equipment by Crop/ Service Type...................... 39
Table 22. Construction and Mining Respondent Pieces of Equipment by Service Type .......... 39
Table 23. Residential Respondent Pieces of Equipment by Region.......................................... 39
Table 24. Residual Respondent Pieces of Equipment by Service Type .................................... 39
Table 25. Distribution of Completed Surveys by Sample Type – Unweighted.......................... 40
Table 26. Commercial Surveys by Sample Type – Sample Frame ............................................ 41
Table 27. Sample Type, Sample Frame and Corresponding SIC Grouping – Commercial
Sectors ................................................................................................................. 41
Table 28. Relative Survey and Sample Size Proportions w/ Response Weightings................... 42
Table 29. Weighted Survey Response Totals ............................................................................. 43
Table 30. Equipment Categories and Counts Reported by Agricultural Region....................... 53
Table 31. Weighted Fuel Type Distribution – Agricultural Sector ............................................ 53
Table 32. Weighted Fuel Type Distribution – Construction/ Mining Sector ............................. 54
Table 33. Weighted Fuel Type Distribution – Residential Sector .............................................. 55
Table 34. Weighted Fuel Type Distribution – Residual Sector .................................................. 56
Table 35. Application Type Distribution – Agricultural Sector, All Equipment........................ 58
Table 36. Application Type Distribution – Construction/ Mining Sector, All Equipment ......... 58
Table 37. Application Type Distribution – Residential Sector, All Equipment ......................... 58
Table 38. Application Type Distribution – Residual Sector, All Equipment ............................. 59
Table 39. Seasonal Activity Distribution by Survey Sector ....................................................... 59
Table 40. Weighted Annual Average Hours/ Year – Agricultural Sector ................................... 60
Table 41. Weighted Equipment Activity Distribution – Agricultural Sector ( Hr/ Yr) ................ 62
Table 42. Average Annual Activity by Region for Diesel Agricultural Tractors....................... 64
Table 43. Weighted Annual Average Hours/ Year – Construction and Mining Sector .............. 64
Table 44. Weighted Equipment Activity Distribution – Construction and Mining
Sector ( Hr/ Yr) ................................................................................................................. 66
Table 45. Weighted Annual Average Hours/ Year – Residential Sector..................................... 68
Table 46. Weighted Equipment Activity Distribution – Residential Sector ( Hr/ Yr) ................. 69
Table 47. Weighted Annual Average Hours/ Year – Residual Sector......................................... 70
Table 48. Weighted Equipment Activity Distribution – Residual Sector ( Hr/ Yr) ..................... 72
Table 49. Weighted Equipment HP Distribution – Agricultural Sector ..................................... 75
Table 50. Weighted Equipment HP Distribution – Construction and Mining Sector ................ 77
Table 51. Weighted Equipment HP Distribution – Residential Sector....................................... 79
Table 52. Weighted Equipment HP Distribution – Residual Sector........................................... 80
Table 53. Model Year Distribution for Selected Equipment – Agricultural Sector .................. 81
Table 54. Model Year Distribution for Selected Equipment – Construction and
Mining Sector ................................................................................................................. 83
Table 55. Model Year Distribution for Selected Equipment – Residential Sector .................... 83
Table 56. Model Year Distribution for Selected Equipment – Residual Sector........................ 84
Table 57. Instrumented Vehicle Daily Activity Profiles ............................................................ 86
Table 58. Fraction of Time at Load and Idle based on RPM...................................................... 91
Table 59. Surrogate Totals – Survey and Statewide Values for Agricultural Sector ................ 94
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Table 60. SSI Employee Size Bins and Assumed Point Estimates – Construction/ Mining and
Residual Sectors.............................................................................................................. 95
Table 61. Surrogate Totals – Survey and Statewide Values for Construction/ Mining Sector .. 97
Table 62. Residual Sector SIC Groupings by Survey Strata ...................................................... 97
Table 63. Surrogate Totals – Survey and Statewide Values for Residual Sector ....................... 97
Table 64. Surrogate Totals – Survey and Statewide Values for Residential Sector ................... 97
Table 65. Equipment Type Incidence per 1,000 Acres – Agricultural Sector............................ 98
Table 66. Equipment Type Incidence per 1,000 Establishments – Construction/
Mining Sector ................................................................................................................. 99
Table 67. Equipment Type Incidence per 1,000 Occupied Households – Residential Sector.. 101
Table 68. Equipment Type Incidence per 1,000 Establishments – Residual Sector................. 101
Table 69. Estimated Statewide Off- road Equipment Populations – Agricultural Sector ......... 103
Table 70. Estimated Statewide Off- road Equipment Populations – Construction/
Mining Sector ............................................................................................................... 104
Table 71. Estimated Statewide Off- road Equipment Populations – Residential Sector ........... 106
Table 72. Estimated Statewide Off- road Equipment Populations – Residual Sector ............... 107
Table 73. County Level Equipment Population Surrogates and Allocation Factors -
Agricultural Sector........................................................................................................ 110
Table 74. County Level Equipment Population Surrogates (# Employees) and Allocation
Factors – Construction/ Mining Sector .......................................................................... 112
Table 75. County Level Equipment Population Surrogates (# Employees) and Allocation
Factors – Residual Sector.............................................................................................. 114
Table 76. County Level Equipment Population Surrogates (# Households) and Allocation
Factors – Residential Sector.......................................................................................... 116
Table 77. Estimated Statewide Off- road Equipment Population – All Sectors........................ 117
Table 78. “ Other” Equipment Category Assignments.............................................................. 119
Table 79. Comparison of Selected Agricultural Equipment Estimates with Agricultural
Census Values ............................................................................................................... 121
Table 80. Average Annual Activity – Estimated Statewide Equipment Population ( Hrs/ Yr).. 123
Table 81. Weighted Average HP – Estimated Statewide Equipment Population..................... 126
Table 82. Weighted HP Distribution – Estimated Statewide Equipment Population............... 128
Table 83. 95% Confidence Intervals - Estimated Statewide Activity Estimates ..................... 131
Table 84. 95% Confidence Intervals - Estimated Statewide HP Estimates............................. 133
Table 85. 95% Confidence Intervals - Estimated Statewide Equipment Population............... 137
Table 86. Current ARB List to Determine Preempt Off- road Applications ............................ 138
Table 87. Equipment Population and Activity Distributions by Application Category for
Estimated Statewide Equipment Totals ........................................................................ 142
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Abstract
Off- road equipment is a major contributor to pollution levels in California, generating ozone
precursors, particulate matter, toxics, and carbon dioxide. These equipment are found in a wide
variety of applications, including lawnmowers, bulldozers, aircraft support equipment, and
portable generators, among other categories. Off- road equipment is used in essentially all types
of businesses, as well as in residential applications. Given the large number of engines involved,
and the highly diverse set of operators, off- road engines have proven more difficult to
characterize and control than many other emission categories.
In order to develop a more comprehensive and consistent data set of engine characteristics and
activity, ARB contracted with Eastern Research Group ( ERG) to conduct a study of off- road
engines less than 175 horsepower operating in the state. The study was conducted in two phases,
with equipment operator surveys and equipment instrumentation techniques developed and tested
under Phase I, and full scale data collection and analysis taking place under Phase II. The study
results include detailed information on equipment characteristics and activity, including
application type, horsepower, and hours per year of use. Surrogates were developed to
extrapolate the survey data to statewide totals, as well as to allocate equipment populations to the
county level. Instrumentation of data loggers was also performed to collect engine- on time, in-use
RPM and exhaust gas temperature data for different types of construction equipment. Based
on the study findings, recommendations are provided for updating the current OFFROAD
emission factor model, as well as the list of federally preempted off- road equipment in
California.
1
Executive Summary
Background
Off- road internal combustion engines are significant contributors to fine particulate matter, air
toxics, and ozone precursor emission inventories in California. Their widespread use across
many applications requires they receive detailed assessment for both emissions inventory
improvement and potential regulatory development in California. The study described in this
report was implemented to develop a comprehensive and consistent profile of off- road
equipment applications, end- users, populations, and activity patterns for equipment less than 175
horsepower ( hp), for the range of different equipment operators across California. The resulting
equipment inventory and instrumentation data can be used to: improve current off- road
equipment counts and emission inventory estimates; determine if the current list of preempted
off- road equipment should be updated; and obtain in- use equipment activity data to help identify
equipment types that may be amenable to various control strategy options.
Methods
The study was conducted in two phases, with Phase I involving a small- scale pilot test of the data
collection effort. The Phase II study ( the subject of this report) implemented the survey and
equipment instrumentation methodology developed under Phase I as a full- scale data collection
effort. Data collection relied on self- reported information from a representative sample of off-road
equipment operators across the state, using questionnaires administered by phone. Working
closely with ARB and key stakeholders, the survey study design was developed by identifying
the businesses and residences to be included in the study, the equipment types, and the data
elements to be collected ( e. g., fuel type, annual hours of operation, hp, and how the equipment is
used, among others). After completion, survey responses were quality assured, and the
equipment population and activity estimates extrapolated to the state level. The effectiveness of
the survey was evaluated in terms of the level of uncertainty associated with the final fleet
estimates, such as average hp and average hours per year.
In a parallel task construction equipment were selected for data logger instrumentation to collect
temporal operation profiles, engine RPM, and exhaust gas temperature. Loggers were installed
on each unit for one week. These data provide daily hours of use as well as inferred operation
mode ( idle versus load) for different equipment types and applications. Such data may be used
to help establish operational profiles for emissions estimation and/ or control assessments.
Results
The equipment operator survey provided an extensive data set for various off- road
equipment/ fuel type combinations, including a number of different equipment characteristic and
operation parameters. Factors were identified and applied to the reported equipment counts to
develop statewide equipment population and activity profiles. An error analysis of the profiles
found the confidence levels for average hp and average hours of operation were relatively precise
for several key equipment categories. Although equipment population estimates had
significantly greater uncertainty, reasonably accurate population, hp, and activity estimates were
obtained for diesel agricultural tractors, compressed gas industrial forklifts, and assorted
2
residential lawn and garden equipment. Activity and hp data may also be utilized for other
equipment categories.
OFFROAD model year distributions may be updated for some of the most common equipment
such as agricultural tractors and compressed gas industrial forklifts. The age distribution for
agricultural tractors was heavily weighted toward older units, with the median age more than 20
years old. Fuel type distributions could also provide useful model updates, particularly for diesel
all terrain vehicles ( ATVs), which are not listed in the current model, and for gasoline
agricultural tractors, which were much more prevalent than assumed. Seasonality data indicate a
substantial variation in activity levels over the year among agricultural, recreational, and lawn
and garden equipment, and could provide a basis for updating the seasonal allocation factors
within the model. Geographic allocation factors were also developed for the distribution of
statewide populations to the county level.
Comparison of the study’s equipment population estimates with independent data sources
indicates a systematic under- reporting of many construction and recreational equipment types.
In addition, several specialty equipment categories were identified by a very low number of
respondents, or not at all by the survey. More notable examples include: airport GSE, rough
terrain forklifts, and TRU. In addition, certain end- user groups appear to be under- represented,
namely commercial lawn and garden companies and public sector fleets. As such, alternative
data sources are likely needed for these equipment types and end users.
Uncertainty associated with both equipment populations and activity levels make preemption
determinations difficult for the different equipment categories. While most activity distributions
appear consistent with ARB’s current preemption list, a few exceptions were identified. ATVs
merit particular evaluation to determine if they should be included with agricultural equipment.
Engine RPM and exhaust gas temperature data were collected on over 70 pieces of construction
equipment. Equipment types included backhoes, loaders, and excavators in both public and
private operation. Engine on- time covered a broad range, from a few hours per week, to heavy
use five or more days per week. Exhaust gas temperature profiles were also highly variable,
even within the same equipment category. Accordingly, generalizations about operation time
and exhaust gas temperature distributions could not be made regarding the construction fleet in
California, or even regarding the specific equipment types instrumented for this survey.
Conclusions
The equipment operator survey successfully collected extensive information on the targeted
equipment fleet operating in California, including data on populations, fuel type, hp and model
year distributions, annual hours of operation, seasonal activity, and user applications. Much of
the equipment population and activity data collected may be integrated into ARB’s OFFROAD
model, thereby improving the state’s emissions estimates for off- road sources. Application data
may also be used to update ARB’s list of preempted off- road equipment less than 175 hp.
Engine instrumentation data may also help design future studies to assess retrofit potentials for
construction equipment operating across the state. Recommendations for additional research
include conducting targeted assessments of construction and recreational equipment using
alternative data sources, and further evaluation of ATV uses for preemption determination.
3
1.0 Introduction
Project Background
Off- road internal combustion engines are significant contributors to the fine particulate matter,
air toxics, and ozone precursor emission inventories in California. These sources operate in a
broad range of applications for an extremely diverse set of industrial and residential end users,
from manufacturing and warehousing companies to recreational boaters. As such, off- road
engines have proven more difficult to characterize and regulate than many other emission
categories such as on- road mobile and major stationary sources. Nevertheless, their widespread
use across so many applications requires they receive detailed assessment for both emissions
inventory improvement and potential regulatory development in California.
The California Air Resources Board ( ARB) has been at the forefront of emissions inventory and
regulatory development in the off- road sector with initiatives such as the Small Off- Road Engine
( SORE) rulemaking, and the recently completed residential lawn and garden equipment
survey.( 1) In addition, in many ways the California OFFROAD emissions model provides more
detailed data on a broad range of off- road engine categories than does the U. S. Environmental
Protection Agency’s ( EPA’s) NONROAD model.
However, much of the equipment population and activity data used in the latest version of
OFFROAD are obtained from a host of different data sources, each with its own advantages and
disadvantages. For example, the MacKay and Company and Power Systems Research ( PSR)
data sets used to compile much of the construction, light commercial, and industrial equipment
category information are based on nationwide surveys, allocated to California using varying
adjustment factors. On the other hand, while the U. S. Department of Agriculture’s ( USDA)
Agricultural Census data are specific to agricultural equipment in California, the Census does not
cover all equipment types in this category. Also, the Portable Equipment Database, which is the
basis for certain portable engine information, relies on voluntary registration and therefore
underestimates equipment counts to some degree. Finally, for many of these data sources the
level of information regarding specific equipment applications and end- users is inadequate for
ARB’s needs.
Ideally all the source category information used in OFFROAD and ARB’s regulatory
development efforts would be based on comprehensive, bottom- up survey data from across
California. In recent years, ARB has taken steps to initiate this process, including development
of an inventory for public sector fleets,( 2) the residential and commercial/ institutional lawn and
garden survey and instrumentation studies, and the survey of Transportation Refrigeration Unit
( TRU) vendors,( 3) among others. In addition, locality- specific inventory information for other
source categories such as aircraft ground support equipment ( GSE) is sometimes provided at the
air district level, in this case often utilizing the Federal Aviation Administration’s ( FAA’s)
Emission Dispersion and Modeling System ( EDMS).
In August 2005, Eastern Research Group ( ERG) was selected to conduct continuing research into
the characteristics of California’s off- road equipment fleet. The study was conducted in two
phases. Phase I covered the tasks associated with planning and designing the study: defining the
equipment types for inclusion, defining the data to be collected on the equipment types,
4
developing a survey plan, and creating a survey instrument and sample. Phase I also included a
small- scale pilot test of data collection and field instrumentation methods to assess their
effectiveness and efficiency. Phase I concluded with documentation of all activities through the
pilot test, with recommendations on methodology refinements for the full- scale study.
The full- scale, Phase II study began after submittal of the Phase I report and written
authorization by ARB. Minor changes to the equipment operator survey and instrumentation
procedures were implemented to improve data collection accuracy and efficiency. The study
results include detailed information on equipment characteristics and activity, including
application type, horsepower, and hours per year of use. Surrogates were developed to
extrapolate the survey data to statewide totals, as well as to allocate equipment populations to the
county level. Instrumentation of data loggers was also performed to collect engine- on time, in-use
RPM and exhaust gas temperature data for different types of construction equipment.
Operator surveys were completed in June of 2007, and equipment instrumentation was
completed in November of 2007. Data post- processing, quality assurance and statistical analyses
were conducted on the resulting data sets. Based on the study findings recommendations were
developed for updating the current OFFROAD emission factor model, as well as the list of
federally preempted off- road equipment in California.
This report summarizes the methodology and findings of Phase II of the study.
Project Objectives
Through this study, ARB desired to develop a comprehensive and consistent profile of off- road
equipment applications, end- users, populations, and activity patterns for the range of different
industrial, public, and residential equipment operators across California. The focus was on off-road
equipment less than 175 horsepower ( hp). Data collection relied on self- reported
information from a stratified random sampling of off- road equipment operators across the state,
using questionnaires administered by phone. Additional in- use activity data was collected
through the deployment and retrieval of data loggers in the field. This approach, utilizing
California- specific, “ bottom- up” data collection, was assumed to provide a more reliable
characterization of equipment types and use patterns than prior “ top- down” efforts, which
commonly rely on national data combined with regional allocation routines.
The resulting equipment inventory and instrumentation data was developed to serve the
following purposes:
· Create and/ or use an equipment categorization scheme consistent with ARB’s
OFFROAD model conventions to facilitate the improvement of the emission
inventory and regulatory development;
· Characterize equipment populations in the various categories and types by fuel
type, engine size, age, annual hours and seasons of use, and the applications of the
equipment;
· Obtain in- use data on equipment activity which can be used by ARB to identify
types of equipment that are amenable to various control strategy options;
· Provide equipment counts that can be used to estimate total numbers of the
equipment at the state and county levels; and,
5
· Determine if the current list of preempted off- road equipment should be updated.
Report Organization
The following sections of this report document the study methodology followed for conducting
the Phase II data collection, and presents the operator survey and equipment instrumentation
results. A discussion of the results, including a statistical analysis and assessment of data set
completeness is then presented. A summary of the major findings of the study are presented
next, along with recommendations regarding potential updates to the OFFROAD model and the
off- road equipment preemption list. Utilization of equipment instrumentation data is also
discussed. Finally, recommendations for future refinement of the resulting data set are provided.
6
2.0 Materials and Methods
Overview
The purpose of the Phase II study was to implement the survey and equipment instrumentation
methodology developed under Phase I as a full- scale data collection effort. Working closely
with ARB and key stakeholders, the Phase I study design was updated to improve survey
response rates and data collection efficiency.
The survey study design was then developed by defining the sample frame ( e. g., the commercial
businesses and residences to be included in the study), equipment types, and the data elements to
be collected. Next steps included designing the corresponding survey instrument to collect the
required data elements, as well as other survey materials ( e. g., survey instructions and advance
letter), and programming the survey questionnaire for data collection via telephone.
The Phase II study data collection effort was conducted from February 23, 2007 through May 25,
2007 using telephone interviewing. In order to obtain missing demographic data in the
Residential Sector for weighting purposes, a small additional data collection effort was
conducted from June 12, 2007 through July 9, 2007 for residential respondents.
Once complete, survey responses were quality assured and otherwise evaluated for
reasonableness. The effectiveness of the survey was also evaluated in terms of overall response
rates, non- response for individual questions, and other factors that could bias the results of the
full- scale survey.
In addition to the survey effort, a parallel task was undertaken to identify candidates for data
logger instrumentation, in order to collect temporal operation profiles, engine RPM, and exhaust
gas temperature. During Phase II, data loggers were installed on pieces of construction
equipment for a period of one week. These data allow for the estimation of daily hours of use as
well as inferred mode ( idle versus load) for a range of different equipment types and
applications. Such data can be used to help establish detailed operational profiles for emissions
estimation and/ or control assessments.
The following sections of this report document the data collection methods for the survey as well
as the instrumentation tasks.
2.1 Equipment Characterization Survey
2.1.1 Sample Frame Development
At the onset of the survey planning process, three broad categories, or sample frames, were
identified to characterize the range of possible off- road equipment operators. Samples of
potential equipment operators would then be derived from these three distinct sampling frames:
· Agricultural frame, to characterize the agricultural industry, consisting of all
farmers and farm management companies in the State of California that report
income from the sale of their crops and/ or management services;
7
· Commercial frame, consisting of California businesses and public entities. This
frame was further disaggregated, using SIC codes, into the following strata for
purposes of manageability and subsequent application of surrogates:
Construction/ Mining, and Other Commercial/ Government entities ( referred to as
the “ Residual” sample in this report);
· Residential frame, consisting of listed and unlisted non- business telephone
exchanges in the state of California.
After consultation with ARB, stakeholder groups, and sample providers, it was determined
during Phase I that additional sample stratification would be necessary to collect sufficiently
detailed data for the different sectors. Agricultural entities were identified by crop type as
reported to the Federal Census Bureau. The following provides a list of the final agricultural
sample strata. 1 For a detailed list of all crop types included in each agricultural stratum, please
see Appendix A.
· Nut
· Row Crop
· Tree Fruit
· Other
· CAFO/ Dairy
· Farm Management2
During Phase I study design planning, agricultural stakeholders raised concerns regarding how
the survey would capture equipment data from farms with “ absentee” owners ( farm owners that
do not reside on the property in question and use a farm management company for all
operations), as well as from farms which contract out some, but not all, of their operations to
another local farmer ( who is not considered a farm management company). These issues were
explored further during the Phase I pilot study through interviews with farmers that provide
services to, or receive services from, other farmers in their community. To ensure equipment
used in these instances was properly captured, farm management firms were included in the
sample frame as a separate category. 3 Further, the questionnaire was designed to capture
equipment owned or leased by individuals ( i. e., not farm management companies) who provided
agricultural services on land owned by other farmers in addition to their own. To collect this
information, the questionnaire asked farmers/ operators about the equipment they own and
operate in California, as opposed to the equipment used specifically on their farm. “ Now, this
1 In order to stratify at this level of detail, the project team used an agricultural database maintained by the US
Department of Agriculture ( USDA). The sample was purchased through a third party that pays a subscription
service for access to the database. The project team received a summary report of crop types grown in California
and aggregated them into the categories shown above.
2 Farm management entities are defined as businesses that perform agricultural activities ( such as harvesting,
plowing, etc.) for other farmers for a fee, as their primary activity.
3 Farm management entities were subsequently re- assigned to one of the remaining strata based on their reported
activity type for the purposes of surrogate expansion.
8
next series of questions will focus only on the equipment contained in your current inventory of
owned or leased equipment that operates in California” [ from telephone interview script]. 4
Agricultural sample frames were subsequently developed using existing databases maintained by
the following commercial sources.
· For non- farm management agricultural entities, the sample frame consisted of an
agriculture database maintained by the US Department of Agriculture ( USDA),
subscribed to by Survey Sampling International ( SSI), a commercial survey
sample vendor. This database contains nationwide coverage for growers of
agricultural crops. In addition to administrative data such as name, address and
phone number, the database lists the following for each grower: crop type,
acreage, and reported annual income from sale of crop.
· For farm management entities, the sample frame was based on the Standard
Industrial Classification ( SIC) database maintained by Dunn and Bradstreet. The
SIC used is a four- digit code that identifies the primary industry sector of which
the company is a member.
Additional sub- stratification was deemed necessary for the remaining user categories. Mining,
logging, and “ recreational” sub- strata were defined within the Construction, Residual, and
Residential strata, respectively, in order to ensure data collection on specialty equipment types.
For further detail on the specific SICs selected for the Agricultural, Construction, and Residual
sample frames see Appendix B.
The Residential frame was partitioned into Recreational ( or “ Target”) and Other ( or “ Non-
Target”), with the Recreational sample defined as households that live in close proximity to
recreational areas, such as a major lake or national recreational area. After consultation with
ARB staff, the following counties were included in the recreational target substratum: El Dorado,
Imperial, Lake, Merced, Napa, and Placer. The areas selected as the basis for the Recreational
sub- strata are also shown in Figure 1.
Although households located in other areas of the state may travel to the designated Recreational
area counties and use their off- road equipment there from time to time, no attempt was made by
the survey to characterize the transient movement of equipment to other regions. This was true
for other survey sectors as well. Therefore equipment identified through the surveys was
assumed to be operated in the county where the associated respondent was located.
4 One option for collecting information on equipment used on a property but is not owned or leased by the
owner/ farmer is to obtain a referral of the name of the operator/ service provider, and then conduct a subsequent
survey with this additional contact. ARB decided against this option for several reasons, including the potential
response error resulting from service providers inaccurately reporting annual/ seasonal activity data for equipment
used on a particular farm, as well as the overall increase in data collection costs to pursue potentially multiple
referrals for a single farm.
9
Figure 1. Location of Recreational Target Sub- Strata
2.1.2 Survey and Sample Size Determination
A total of 1,200 completed surveys were originally planned for the full- scale study. Table 1
presents the goals of the study for the total number of completed interviews, taking into
consideration the surveys completed in the Phase I pilot study. The table first presents the
original study goals followed by the revised study goals based upon the pilot results. The
precision estimates refer to the confidence interval for the total number of completes at the 95%
confidence level.
Table 1. Pilot and Full Study Completes By Sample Type and Sub- Strata
Original Full Study Revised Full Study
Sample Type
Phase I
Pilot
Completes Full Study
Total
Pilot + Full Precision Full Study Total
Pilot + Full Precision
Agriculture 29 271 300 5.8 246 275 6.4
Construction 10 240 250 6.3 215 225 6.7
Residual 12 288 300 5.8 263 275 6.2
Residential 12 348 350 5.3 313 325 5.7
Total 63 1,147 1,200 2.9 1,037 1,100 3.0
10
The total completed surveys were reduced from 1,200 to 1,100 as a result of the response rates in
the Phase I pilot study. However, perhaps due to the changes made to the survey procedure
based on ARB and stakeholder input, interviewing productivity was higher than anticipated and
the revised study goals were exceeded for all Sample Types ( see Table 9 for details).
At the onset of a survey study it is generally unknown how many sample records would be
required to obtain the target number of survey completions for each strata and sub- strata.
“ Ineligible” sample can arise for a number of reasons – establishments are no longer in business;
they have moved operations out of state; the business was bought out and now is listed under a
new owner or name; etc. Moreover, not all establishments will operate off- road equipment.
Finally, not all establishments will ultimately cooperate with the study. For these reasons it is
important to obtain substantially more sample than the targeted number of completed surveys.
The sample needs estimated for the full study are presented in Table 2. Estimates are based on
SIC lists obtained from Dunn and Bradstreet for the State of California, US Census data, past
survey experience using listed and unlisted sample, and Phase I survey results including contact
and non- contact rates, screening response rates, eligibility and survey completion rates.
Table 2. Estimated Number of Sample Records Needed to Meet Survey Targets
Sample Type Sub- strata Minimum Quota Assumed
Completes
Completion
Rate
Total
Sample
Nut Crop 34
Row Crop 45
Tree Fruit 29
Other Crop 46
CAFO/ DAIRY 12
Agriculture
Farm Management 7
275 3.5% 7,000
Construction 210
Construction
Mining* 5
225 2.4% 9,000
Logging* 5
Residual
Other 258
275 4.0% 6,500
Recreational* 75
Residential
Other 145
325 2.7% 11,500
Total 1,100 3.1% 34,000
* The universe totals for these sub- strata are low and minimum quotas could not be applied to the
corresponding sample types.
Completion rates refer to the fraction of all respondents in the sample that are eligible to
participate and actually complete the survey. Response rates refer to the fraction of eligible
respondents that actually participate in the survey. Surveys are adjusted for low/ high response
rates using analytic weights, as discussed in Section 3.1.4.
Table 2 also shows target quotas by sample subtype. Setting minimum quotas ensures that the
sample is representative of all the sample subtypes. Minimum quotas were set such that they met
the following criteria:
11
· The minimum quotas for each sample subtype should be proportional to the
distribution of the count of completes by sample subtypes within a sample type.
· The sum of the minimum quotas by sample subtypes within a sample type should
represent 70% of completes required for that sample type. This will ensure that
the sample type is well represented within each sample subtype.
When the minimum quota level defined above is reached for each sample subtype, the remaining
completes required for the full study could be met by completes from sample subtypes that are
easier to obtain. This approach ensured that the sample is well represented within each sample
type and within the available budget. In addition, since the actual call lists were developed
randomly from within each sample subtype, and since response weights were ultimately used to
adjust for non- response bias ( see Section 3.1.4), the final weighted data set was also
representative of the sample universe as a whole. Maintaining this representativeness in the final
data set was a primary goal of the study methodology itself.
This methodology works well for strata that are characterized by robust universe counts such as
Agriculture. However, when this methodology is applied to strata with small universe counts
( particularly Mining and Logging), the resulting minimum quotas are too small to ensure any
type of statistical validity. As such, in lieu of using the same method for establishing minimum
quotas for these substrata, a different approach was necessary, as described below.
1) Construction and Mining Stratum. This stratum is characterized by one
substratum that has a very high universe count ( Construction) and one substratum
that has a very low universe count ( Mining). As such, applying the “ minimum
quota” methodology would result in a minimum quota of 1 for the Mining
substratum, which is not recommended. Rather, known sample performance
parameters from the pilot survey and known universe counts were used to identify
a quota of 5 completed surveys for the Mining substratum, with the balance
coming from the Construction substratum ( 210).
2) Residual Stratum. Similar to Construction and Mining, this stratum is
characterized by one substratum that has a very low universe count ( Logging) and
one substratum that has a very high universe count ( Residual). To prevent a very
small cell size for the Logging substratum, known sample performance
parameters from the pilot survey and known universe counts were used to identify
a quota of 5 completed surveys for the Logging substratum, with the balance
coming from the Residual substratum ( 258).
3) Residential Stratum. This stratum is fundamentally different from the others
since the sampling element is a household, not a commercial establishment.
Similar to the method implemented with the Agriculture Stratum, a Residential
minimum quota was established for the Residential substratum such that the
minimum quota represented 70% of the completes required for that sample type.
Upon review of pilot sample performance parameters, it was decided to have one
third of the minimum quota come from the Recreational target substratum, with
the balance coming from the remainder of the residential substratum.
12
The generation of SIC- based samples involved providing a list of appropriate SIC codes to SSI
for each sample type, as well as the number of requested sample records. Samples were then
randomly selected from the SIC database by SSI and delivered electronically for further
processing. SSI generated the non- farm management agriculture sample in a similar manner by
randomly querying the USDA database until the specified number of records by crop type and
farm size had been generated. The files were then delivered electronically.
Upon receipt, the electronic sample was processed for dialing by partitioning the sample into
“ replicates,” or subsamples, of the main sample. Each replicate ranged in size from 67 to 250
sample pieces, with each replicate containing sample of the same sample strata. The database
contained non- address related information ( except first and last name), phone number and
geographic identifier ( census tract). The database also contained a unique sample number to link
each record between databases and track each record throughout the survey process.
2.1.3 Survey Instrument Design
The survey instrument ( or questionnaire) contained approximately 20 questions. The first series
of questions establishes eligibility ( owning and/ or leasing at least one piece of off- road
equipment with a maximum horsepower rating of less than 175), then proceeds with the
substantive part of the data collection effort. In addition to collecting details on the numbers and
types of equipment contained in a respondent’s inventory, the survey also asks respondents for
the seasonal and annual use of each piece of equipment, as well as details on fuel type,
horsepower and displacement, etc. These data fields were selected to be consistent with the key
data needs of the OFFROAD model. Information on primary and secondary applications of the
equipment was gathered as well, to assess the accuracy of ARB’s current off- road equipment
preemption list.
Cognitive testing5 of a draft version of the questionnaire was conducted during Phase I. Minor
adjustments to question wording and flow were made based on the cognitive test results. In
addition, to facilitate respondent completion, the survey instrument was tailored to each specific
Sample Type. For instance, example equipment categories were made appropriate for
construction, residential, and agricultural respondents.
2.1.4 Updates to Phase I Study Design
Based on the findings of the Phase I study it was determined that the advance letter and mail
out/ internet version of the survey were not effective in improving response rates, and were
withdrawn from the Phase II study design. In addition, a number of edits were made to the
questionnaire to improve organization and comprehensibility, including the following:
5 A cognitive interview is a preliminary test of a draft survey questionnaire with persons that possess similar characteristics to the
survey’s intended audience, involving in- person interviewing. The testing objectives are related to the question- answering
process for potentially complex questions, assessing the respondents’ ability to provide an answer by examining their
comprehension of questions, and their ability to retrieve relevant information from memory. Cognitive interviews are also used
to assess the adequacy of the questionnaire flow ( structure and design).
13
· The screening questions were rearranged and restructured so that eligibility would
be established at the onset of the survey;
· The definition of target equipment was refined to read “ Off- road Vehicle or
Off- road Equipment means any non- stationary device used off the highways and
powered by an internal combustion engine or electric motor, including equipment
such as portable generators”;
· Two questions were deleted because the pilot study revealed that the flagging for
large and small inventories was unnecessary. Not a single “ large inventory”
respondent opted to complete the survey using an alternative survey approach;
· Text was added to prompt respondents to confirm seemingly anomalous
equipment application types ( e. g., recreational equipment claimed to be used in
agricultural activities); and,
· References to “ compressed natural gas” were changed to “ natural gas”.
In addition, based on input from the agricultural stakeholder group nurseries were moved from
the Agricultural to the Residual sample frame ( see next section), and CAFO/ Dairy respondents
were asked for the number of head of cattle rather than acreage ( to facilitate more accurate
surrogate expansion of the results).
A copy of the final survey instrument is provided in Appendix C.
2.2 Equipment Instrumentation
As part of the effort to characterize off- road engine operation, data loggers were to be installed to
record selected engine parameters on pieces of equipment operated in the construction and
mining sector in California. At the start of the study, ARB determined to limit instrumentations
to equipment in the construction and mining sector. This limitation was made in part due to the
extremely diverse equipment and application types within the agricultural and residual sectors.
In addition, the construction and mining sector is heavily dominated by large diesel equipment,
and therefore is a predominant contributor to total nitrogen oxide ( NOx) emissions from off- road
engines.
In Phase I of this assessment, data loggers were installed on two pieces of construction
equipment, one with a mechanically controlled diesel engine, and one with a computer controlled
diesel engine, for a period of one week in order to establish instrumentation and data processing
protocols. At the request of ARB, ERG modified the Phase I instrumentation protocol to
incorporate collection of exhaust gas temperature data in addition to engine on- time and RPM
under Phase II for more than 70 pieces of construction equipment. The resulting operation
profile can be used to help assess the potential effectiveness of various retrofit options ( e. g.,
diesel particulate filters and diesel oxidation catalysts).
2.2.1 Data Logger Characteristics
During Phase I a data logger made by Clēaire was chosen to log engine parameters. The Clēaire
logger was selected because it is normally used to monitor diesel engine parameters, as well as to
operate emissions control systems that can be retrofit onto diesel vehicles. Therefore it has many
more capabilities than simply recording RPM data. The main parts of the Clēaire logger system
14
are shown in Figure 2. The gray box contains the logic and memory of the data logger. The
various black and blue umbilicals connected to the gray box are used to transmit engine data,
emission control system data, and to power the logger. In Phase II three umbilicals were always
used, one to transmit the RPM signal to the logger, one to power the logger, and one to transmit
exhaust temperature. The unused umbilicals were secured safely out of the way during data
logging operations.
Figure 2. Clēaire Data Logger System
( Source: Clēaire)
2.2.2 Sensor Installation
RPM was recorded using two methods. The preferred method utilized a Hall- effect sensor
installed in the bell- housing of the engine to sense the teeth of the flywheel as they pass the
sensor during engine operation ( see Figure 3). Since the flywheel is directly connected to the
crank- shaft of the engine, its rate of spin is directly proportional to the RPM of the engine. This
method required an accessible, threaded port of the proper size in the engine’s bell- housing.
Unfortunately, such a port was often not available. Accordingly, a second method of RPM
detection used the Hall- effect sensor to determine the rate of spin of an idler pulley on the
alternator belt of the engine. Since the alternator belt is driven by the crank- shaft of the engine,
its speed is also directly proportional to the RPM of the engine. The idler pulley was fashioned
like the rubber wheel of an in- line skate, with shielded ball bearings that come with the wheel,
and a bolt ( used as a shaft for the pulley). Heavy upholstery tacks were pushed into the rubber
wheel in a symmetric pattern to provide the Hall- effect sensor moving metal objects to sense as
the wheel rolled on the belt. An installed idler pulley RPM sensor is shown in Figure 4.
15
Figure 3. Hall- Effect Sensor Installed in Bell- Housing of Engine
Figure 4. Idler Pulley/ Hall- Effect Sensor Assembly
16
RPM was calibrated in the field using the RPM readout and the engineering judgment of the
installers ( both of whom were mechanical engineers). This method was considered adequate to
differentiate between engine idle and loaded modes of operation. A more precise calibration of
RPM would have been required in order to fully quantify engine load, however.
Exhaust temperature was typically monitored at the exit of the exhaust pipe. A thermocouple
( type K) was inserted into the exhaust stream, approximately 3- inches into the exhaust pipe. The
end of the thermocouple was kept from touching the interior of the exhaust pipe by rigidly
securing the base of the thermocouple to a spring ‘ stand- off’ on the exterior of the pipe, then
bending the thermocouple into a ‘ U’ shape so it extended into the exhaust pipe without touching
the interior wall. In some cases, exhaust temperature thermocouples were already installed in the
exhaust system ( for example, when a particulate filter system had been retrofitted onto the
vehicle). In these instances, ERG simply tapped into the existing exhaust thermocouple.
2.2.3 Logger Installation and Removal Procedures
ERG developed a standard procedure to ensure consistent quality of the installation and resulting
data. To begin installation, the installer familiarized himself with the vehicle and, if necessary,
had an operator demonstrate safe engine starting and stopping procedures. Then the data logger,
sensors, and signal and power wires were laid out and loosely attached to temporarily secure
them. Then the system was tested to ensure all components were working properly. The
calibrated RPM was required to fall between 650 and 850 at idle, and between 1,500 and 3,000 at
maximum governed engine speed. The thermocouple reading had to be reasonable when held in
ambient conditions, with the exhaust above 200 degrees C at high RPM. After RPM and
temperature readings had been quality assured in the field, the installer secured all connections,
wires, and the logger and connections safely out of the way of all engine operations and
maintenance.
When possible the installer would periodically check active data logging systems already on the
engine to determine if any repairs or recalibrations were necessary. In the cases where a logger
system failed, ERG would diagnose the problem and re- start the logging. At least one week of
logging was required before a system was removed. In those cases where a system had to be
removed in less than one week, another piece of equipment was found and the logging process
was re- started.
A copy of the field installation and retrieval procedure is provided in Appendix D.
2.2.4 Equipment Sample
ARB specified a list of equipment types for instrumentation during Phase II. This list was based
upon a review of previous off- road equipment surveys and internal discussions among ARB
staff.( 4) The preferred equipment list is shown in Table 3. Three age bins were specified as
desirable: 1995 and older, 1996 to 2001, and 2002 and newer, although no specific quotas were
established for the different bins.
17
Table 3. Target Construction Equipment Categories for Instrumentation
Backhoe Tractor
Loader Rubber Tired Loader
Excavator Claw Tractor
Trencher Roller
Grader ( Construction) Grader ( Snow)
Paver Scraper
Chipper/ Stump Grinder Other*
* Based on ARB approval.
ERG negotiated with many fleet owners to identify equipment for instrumentation. With a few
notable exceptions, publicly owned fleets tended to be the most cooperative and willing to
participate. A list of the publicly owned fleets contacted for this study is shown in Appendix E.
The three private fleets participating in the study were owned by Teichert Construction, Doug
Veercamp Construction, and Hobday Equipment Rental. Twelve other private fleet owners were
contacted for participation in the study and either did not have equipment needed for the study or
were unwilling to participate.
Most installations occurred in the Sacramento area. However, installation locations ranged from
Woodland in the north to Fresno in the south, and from Rescue in the east to Vacaville in the
west. Figure 5 indicates the areas where installations were performed. Areas of installation are
indicated by red, dashed ovals. All but one area ( Stockton) resulted in at least one calendar week
of contiguous logging.
18
Figure 5. Equipment Instrumentation Sites
( www. google. com)
The original logging schedule was scheduled for the summer of 2007. However, various
logistical, equipment, and participant issues resulted in significant delays to the schedule. As a
result, logger installations occurred from the beginning of April until the end of November of
2007. Figure 6 shows the days during which loggers were operational.
19
Figure 6. Calendar Showing Days of Logger Operation
2007 Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu
April 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
May 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
June 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
July 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
August 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
September 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
October 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
November 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
A total of 75 pieces of equipment had an operational logger installed for a contiguous week.
Table 4 summarizes the pieces of equipment successfully instrumented for this project. The Unit
ID corresponds to the date of installation. If more than one piece was installed on a given day,
the serial number at the end of the ID differentiates between them. The “ Activity Days” column
lists the dates which produced activity data for the piece of equipment. Unit Type was assigned
using the nomenclature provided by ARB. Only a few pieces were operated every day during
the 7 days of installation. However, most pieces operated during 3 or more days of the week.
This sample may have been biased toward equipment that operates less frequently than average.
Fleet operators may have directed ERG installers to the less active pieces to minimize
disruptions in their schedules.
As seen in the table there was substantial sampling on loaders, backhoes, and compactors due to
their relative abundance and availability during the project. Unfortunately, no snow graders,
rollers, pavers, or trenchers were successfully instrumented.
A more detailed discussion of the data logger findings is provided in Section 3.2.
20
Table 4. Instrumented Equipment Detail
Unit ID Install Start Activity Days Install End Unit Type Make Model
Engine
Year
20070401- 1 4/ 1/ 2007 1,2 4/ 7/ 2007 Loader Caterpillar IT 38G 2004
20070503- 1 5/ 3/ 2007 3,4,8,9 5/ 9/ 2007 Loader Case W11 1981
20070508- 1 5/ 8/ 2007 8,9,10,11 5/ 14/ 2007 Backhoe Deere 310SG 2004
20070515- 1 5/ 15/ 2007 15,16,17,18 5/ 21/ 2007 Backhoe 1998
20070515- 2 5/ 15/ 2007 15,16,17,18,21 5/ 21/ 2007 Grinder Peterson Pacific 5400 2002
20070515- 3 5/ 15/ 2007 16,17,18 5/ 21/ 2007 Loader Caterpillar 1983
20070516- 1 5/ 16/ 2007 16,17,21 5/ 22/ 2007 Loader Deere 640
20070517- 1 5/ 17/ 2007 17,18,22 5/ 23/ 2007 Backhoe Terex TX760 2002
20070521- 1 5/ 21/ 2007 23,24,25 5/ 27/ 2007 Compactor Caterpillar 825C
20070522- 1 5/ 22/ 2007 22,24,25,26,27 5/ 28/ 2007 Screener Trommel 2006
20070522- 2 5/ 22/ 2007 22,23,24,25 5/ 28/ 2007 Backhoe Case 1997
20070523- 1 5/ 23/ 2007 29 5/ 29/ 2007 Loader Komatsu WA250L 2005
20070524- 1 5/ 24/ 2007 25,29,30 5/ 30/ 2007 Backhoe Deere 310SE 2000
20070526- 1 5/ 26/ 2007 30,31 6/ 1/ 2007 Loader Caterpillar 953C 1999
20070529- 1 5/ 29/ 2007 29,30,31,1,4 6/ 4/ 2007 Grinder
20070529- 2 5/ 29/ 2007 29,30,31,1,2 6/ 4/ 2007 Compactor Caterpillar 836G 2004
20070530- 1 5/ 30/ 2007 30,31,1,2,3,4,5 6/ 5/ 2007 Grader Deere 872D 2005
20070530- 2 5/ 30/ 2007 30,31,1,2,4,5 6/ 5/ 2007 Loader Volvo L150C
20070531- 1 5/ 31/ 2007 31,1,2,3 6/ 6/ 2007 Backhoe
20070601- 1 6/ 1/ 2007 4 6/ 7/ 2007 Backhoe Deere 410G 2004
20070602- 1 6/ 2/ 2007 4,5,6 6/ 8/ 2007 Backhoe Caterpillar 430 EIT 2006
20070602- 2 6/ 2/ 2007 3,4,5,6,7,8 6/ 8/ 2007 Loader Caterpillar IT 38G 2001
20070604- 1 6/ 4/ 2007 4,5,6,7,8 6/ 10/ 2007 Dozer Caterpillar D9R 1996
20070605- 1 6/ 5/ 2007 5,6 6/ 11/ 2007 Screener
20070605- 2 6/ 5/ 2007 5,6,7,8,10,11 6/ 11/ 2007 Compactor Caterpillar 836G 2001
20070605- 3 6/ 5/ 2007 5,6,7,8 6/ 11/ 2007 Backhoe Deere 410G 2002
21
Unit ID Install Start Activity Days Install End Unit Type Make Model
Engine
Year
20070606- 1 6/ 6/ 2007 6,7,8,14 6/ 14/ 2007 Loader Volvo L150E
20070606- 2 6/ 6/ 2007 6,7,8,9,10 6/ 13/ 2007 Rubber Wheel Loader Caterpillar 980 1998
20070607- 1 6/ 7/ 2007 12 6/ 13/ 2007 Backhoe
20070609- 1 6/ 9/ 2007 9,10,11,12,13,14,15 6/ 15/ 2007 Loader Caterpillar 953C 2000
20070612- 1 6/ 12/ 2007 13 6/ 18/ 2007 Backhoe Deere 710D 1998
20070614- 1 6/ 14/ 2007 14,15,16,17,18,19,20 6/ 20/ 2007 Dozer Caterpillar D9R 2002
20070615- 1 6/ 15/ 2007 15,16,18,21 6/ 21/ 2007 Loader Caterpillar 1986
20070616- 1 6/ 16/ 2007 16,17,18,19,20 6/ 22/ 2007 Loader Caterpillar 950G 2002
20070622- 1 6/ 22/ 2007 22,23,24,25,26 6/ 28/ 2007 Loader
20070624- 1 6/ 24/ 2007 25,26 7/ 1/ 2007 Loader Caterpillar 966E 1990
20070628- 1 6/ 28/ 2007 28,29,2,4 7/ 4/ 2007 Backhoe Deere 310SE 2000
20070705- 1 7/ 5/ 2007 5,6,7,9,10,11,12 7/ 12/ 2007 Backhoe Deere 310SE 2000
20070709- 1 7/ 9/ 2007 11,12,13 7/ 15/ 2007 Rubber Wheel Loader Komatsu WA250L 2005
20070716- 1 7/ 16/ 2007 17,19,20 7/ 22/ 2007 Loader Caterpillar 966 2003
20070718- 1 7/ 18/ 2007 18,19,20,21,22,23,24 7/ 24/ 2007 Loader Caterpillar 914G
20070729- 1 7/ 29/ 2007 29,30,31,1,2 8/ 4/ 2007 Backhoe Deere 410SG 2001
20070803- 1 8/ 3/ 2007 3,4,6,7,9 8/ 9/ 2007 Wheel Loader
20070823- 1 8/ 23/ 2007 23,24,27,29 8/ 29/ 2007 Backhoe Deere 310SG 2004
20070824- 1 8/ 24/ 2007 24,28,30 8/ 30/ 2007 Wheel Loader Komatsu WA450
20070824- 2 8/ 24/ 2007 24,25,27,28,29,30 8/ 30/ 2007 Scraper Caterpillar 623F
20070824- 3 8/ 24/ 2007 24,27,29,30 8/ 30/ 2007 Dozer Komatsu D155AX
20070826- 1 8/ 26/ 2007 30,31 9/ 1/ 2007 Compactor Caterpillar 815F
20070830- 1 8/ 30/ 2007 30,31,4 9/ 5/ 2007 Backhoe
20070831- 1 8/ 31/ 2007 31,4,5,6,7 9/ 7/ 2007 4WD Tractor Root Plow
20070831- 2 8/ 31/ 2007 4,5 9/ 6/ 2007 Wheel Loader Caterpillar 980C 1986
20070831- 3 8/ 31/ 2007 31,4,5,6,7 9/ 7/ 2007 Scraper Caterpillar 623 2001
20070831- 4 8/ 31/ 2007 31,4,6,7 9/ 7/ 2007 Dozer Caterpillar D9R 2001
20070906- 1 9/ 6/ 2007 6,7,10,11,12,13,14 9/ 14/ 2007 Excavator Komatsu PC400 2004
22
Unit ID Install Start Activity Days Install End Unit Type Make Model
Engine
Year
LC
20070907- 1 9/ 7/ 2007 7,11,12,13,14 9/ 14/ 2007 Claw Tractor/ Loader Case 521 DXT
20070913- 1 9/ 13/ 2007 17,18,19 9/ 19/ 2007 Excavator Volvo EC290B 2006
20070917- 1 9/ 17/ 2007 17,20,24,25 9/ 25/ 2007 Claw Tractor/ Loader
20070919- 1 9/ 19/ 2007 20,21,24,25 9/ 26/ 2007 Excavator Komatsu
PC400
LC 2004
20070923- 1 9/ 23/ 2007 27,29 9/ 29/ 2007 Compactor
20070926- 1 9/ 26/ 2007 27,28,2 10/ 2/ 2007 Claw Tractor/ Loader
20070930- 1 9/ 30/ 2007 1,3,4 10/ 6/ 2007 Wheel Loader
20071004- 1 10/ 4/ 2007 4,8,9,10,11 10/ 11/ 2007 Claw Tractor/ Loader
20071010- 1 10/ 10/ 2007 10,11,16 10/ 17/ 2007 Rubber Wheel Loader Caterpillar 950G 2002
20071018- 1 10/ 18/ 2007 18,19,20,22,23,24 10/ 24/ 2007 Rubber Wheel Loader Komatsu WA250L 2006
20071025- 1 10/ 25/ 2007 25,26 10/ 31/ 2007 Compactor Pactor 3- 30 1984
20071101- 1 11/ 1/ 2007 1,2,5 11/ 7/ 2007 Compactor Caterpillar 825G
20071108- 1 11/ 8/ 2007 8,13,14 11/ 14/ 2007 Compactor Caterpillar 815B 1986
20071112- 1 11/ 12/ 2007 12,14,15,17 11/ 18/ 2007 Rubber Wheel Loader Caterpillar 980C 1987
20071115- 1 11/ 15/ 2007 15,16,17,18,19 11/ 21/ 2007 Compactor Pactor 3- 30 1982
20071124- 1 11/ 24/ 2007 24,30 11/ 30/ 2007 Compactor Caterpillar 825G 1996
23
3.0 Results
The findings for the equipment survey and instrumentation tasks under Phase II of the study are
presented below.
3.1 Equipment Survey Results
The data collected during the survey effort provides detailed information for a wide variety of
off- road equipment types and end- users. The following sections provide general descriptive
statistics as well as in- depth statistical analyses regarding equipment populations and
characteristics directly influencing emissions estimates, including fuel types, activity profiles, hp
distributions, and age distributions, among other factors.
3.1.1 Post- Processing and Quality Assurance
Once the survey results were compiled, formatted, and cleaned by the data collection
subcontractor, the equipment data were subjected to additional range checks and quality
assurance measures to ensure the quality and accuracy of the data set. Evaluations focused on
assuring accurate assignment of equipment to appropriate OFFROAD model equipment
categories, identification of missing hp values, refinement of equipment application assignments,
excluding any non- target equipment, and identification and treatment of suspected outliers. The
following describes the various quality assurance measures applied to the survey data set.
Equipment Category Assignments
ERG used the equipment list in ARB’s OFFROAD equipment file to map respondent equipment
descriptions to the standardized equipment listing. Assignments were based on the contractor’s
familiarity with off- road equipment types as well as web searches. There were many instances
where a corresponding equipment type could not be found in ARB’s OFFROAD file. In these
instances, the original respondent equipment type description was retained. Another exception
involved equipment that was electrically powered or manually operated. In these cases,
regardless of equipment type, an equipment type of “ Electric” or “ Manual” was assigned and
these records were set aside from the rest of the data tables for later ARB evaluation. Table 5
summarizes the electric equipment type descriptions reported by survey sector.
Table 6 provides a list of unique respondent equipment types and the corresponding ARB
equipment type. Non- electric equipment for which no clear category match was established
were subsequently grouped together in “ Miscellaneous” categories, as discussed later in this
report ( see Table 7).
24
Table 5. Electric Equipment Type Descriptions by Survey Sector
Equipment Category Agricultural Construction & Mining Residential Residual Total
Air Compressor( s) 93 3 151 247
Air Conditioner 1 1
Air Scrubber 1 1
Bailer( s) 2 2
Belt Sander 1 1
Bench Saw 1 1
Bender 1 1
Book Maker 2 2
Brakes 2 2
C & C Machine 5 5
Car Lift 2 2
Cart( s) 4 4
Cement Mixer 1 1
Centrifuge 1 1
Chainsaw( s) 8 8
Compressor 1 1
Cutter 2 2
Dehumidifier 2 2
Drill Motor 1 1
Drill( s) 18 6 6 30
Dynamometer 1 1
Forklift( s) 1 15 16
Generator Set( s) 1 1 2
Golf Cart( s) 4 1 2 20 27
Hydro- pump 1 1
Ice- Machines 2 2
Irrigation Set( s) 1 1
Jack Hammer 5 5
Lathe 1 1
Lawn Mower( s) ( Walk Behind) 17 17
Leaf Blower( s) ( Hand Held) 29 1 30
Man Lift( s) 2 3 5
Mill 5 5
Milling Machine 5 5
Orbital Sander 2 2
Outside Vacuum 1 1
Pallet Jack 1 1
Panel Saws 1 1
Pipe Threader 17 17
Polisher 1 1
Precrusher 1 1
Pressure Washer( s) 1 1
Pump( s) 1 1
Reciprocal Saw 1 1
Refrigeration Compressors 8 8
Sand Blaster 1 1
25
Equipment Category Agricultural Construction & Mining Residential Residual Total
Saw 3 3
Screw Driver 4 4
Shop Vacuum 2 2
Skill Saw 1 3 4
Splitter 1 1
Spray Booth 1 1
Sprayer( s) 3 1 4
Table Classifier 1 1
Table Saw 1 4 5
Tile Saw 1 6 7
Trimmer/ Edger/ Brushcutter 54 54
Vacuum 3 3
Vertical Milling Machine 5 5
Water Extractor 1 1
Welder( s) 6 7 13
Well 1 1
Wire Puller 1 1
Zapper Saw 1 1
Total 7 172 135 266 580
26
Table 6. Respondent Equipment Types and Corresponding ARB Equipment Type Assignments
Respondent Equipment Types ARB Equipment Mapping Respondent Equipment Types ARB Equipment Mapping
Aerial Lift( s) Aerial Lifts Mill Mill*
Ag Wells Ag Wells* Minibike( s) Minibikes
Agricultural Mower( s) Agricultural Mowers Mixer Cement and Mortar Mixers
Agricultural Tractor( s) Agricultural Tractors Motor Boat Vessels w/ Outboard Engines
Air Compressor Air Compressors Off- Highway Truck( s) Off- Highway Trucks
Air Compressor( s) Air Compressors Off- Road Motorcycle( s) Off- Road Motorcycles Active
Air Conditioner Air Conditioner Orbital Sander Orbital Sander*
Air Scrubber Air Scrubber* Out Board Engine Vessels w/ Outboard Engines
All Terrain Vehicle( s) All Terrain Vehicles ( ATVs) Outside Vacuum Leaf Blowers/ Vacuums
Backhoe( s) Tractors/ Loaders/ Backhoes Pallet Jack Pallet Jack*
Bail Hauler Bale Hauler* Panel Saws Saw*
Bailer( s) Balers Paver( s) Pavers
Balancer Balancer* Paving Equipment Paving Equipment
Belt Sander Belt Sander* Personal Water Craft Personal Water Craft
Bench Saw Saw* Pick Up Onroad*
Bender Bender* Pipe Threader Pipe Threader*
Boat Vessels w/ Outboard Engines Pipe Threading Machine Pipe Threading Machine*
Boat Motor Vessels w/ Outboard Engines Plaster Mixer Cement and Mortar Mixers
Boat Outboard Motor Vessels w/ Outboard Engines Polisher Polisher*
Bob Cat Skid Steer Loaders Precrusher Precrusher*
Bobcat Skid Steer Loaders Pressure Washer( s) Pressure Washers
Book Maker Book Maker* Pump( s) Pumps
Brakes Brakes* Reciprocal Saw Saw*
Brush Cutter( s) Trimmers/ Edgers/ Brush Cutters Refrigeration Compressors Compressor ( Other) *
Bulldozer( s) Crawler Tractors Riding Lawn Mower Front Mowers
C And C Machine C and C Machine* Riding Lawn Mower( s) Front Mowers
Car Lift Car Lift* Roller( s) Rollers
Cargo Loader( s) Cargo Loader Sand Blaster Sand Blaster*
Cart( s) Cart Saw Saw*
Caterpillar Unknown Caterpillar* Scraper( s) Scrapers
Cement Mixer Cement and Mortar Mixers Screw Driver Screw Driver*
Centrifuge Centrifuge* Service Truck( s) Service Truck
27
Respondent Equipment Types ARB Equipment Mapping Respondent Equipment Types ARB Equipment Mapping
Chainsaw( s) Chainsaws Shaker Shaker*
Chainsaw( s) ( Lt 5 Hp) Chainsaws Shop Vacuum Shop Vac*
Champ Champ* Shredder( s) (> 5Hp) Shredders
Chipper Chippers/ Stump Grinders Skid Steer Loader( s) Skid Steer Loaders
Chop Bag Shop Vac* Skidder( s) Skidders
Combine( s) Combines Skill Saw Saw*
Compactor Rollers Skytrack Aerial Lifts
Compressor Compressor ( Other) * Snow Blower Snowblowers
Concrete Saw Concrete/ Industrial Saws Snow Mobile Snowmobiles Active
Crane( s) Cranes Specialty Vehicle Cart( s) Specialty Vehicles Carts
Cultivator Tillers Splice Splice*
Cut Off Saw Concrete/ Industrial Saws Splitter Splitter*
Cutter Cutter* Spray Booth Electric*
Dehumidifier Dehumidifier* Sprayer( s) Sprayers
Diesel Motor Diesel Motor* Spreader Spreader*
Dipswitch Signal Boards Storm Grinders Storm Grinder*
Dirt Compactor Rollers Strain Trimmer
Trimmers/ Edgers/ Brush
Cutters
Dirt Remover Dirt Remover* Swamp Cooler Electric*
Drill Motor Drill Motor* Swather( s) Swathers*
Drill( s) Drills* Sweeper Sweepers/ Scrubbers
Drilling Rig( s) Bore/ Drill Rigs Sweeper( s)/ Scrubber( s) Sweepers/ Scrubbers
Dynamometer Dynamometer* Table Classifier Table Classifier*
Edger Trimmers/ Edgers/ Brush Cutters Table Saw Saw*
Electric Lawn Mower Electric* Tamper Tampers/ Rammers
Electric Skill Saw Electric* Terminal Tractor( s) Terminal Tractors
Electric Weed Whacker Electric* Thatcher Thatcher*
Excavator( s) Excavators Tile Cutter Saw*
Feed Feeder Feed Feeder* Tile Saw Saw*
Fire Pump Pumps Tiller( s) Tillers
Fishing Boat Vessels w/ Outboard Engines Tire Balancer Tire Balancer*
Industrial forklift( s) Industrial forklifts Tire Changer Tire Changer*
Fuel Pump Pumps Tractor( s) Tractors/ Loaders/ Backhoes
Generator Set( s) Generator Sets Transportation Refrigeration Transport Refrigeration Units
28
Respondent Equipment Types ARB Equipment Mapping Respondent Equipment Types ARB Equipment Mapping
Unit( s)
Golf Cart Golf Carts Trash Pumps Pumps
Golf Cart( s) Golf Carts Trencher( s) Trenchers
Grader( s) Graders Trimmer
Trimmers/ Edgers/ Brush
Cutters
Harvester( s) Combine( s) Underground Saw Saw*
Hedge Trimmer Trimmers/ Edgers/ Brush Cutters Vacuum Vacuum*
High Ranger Bucket Truck Aerial Lifts Vacuum Cleaner Vacuum*
Hot Tar Pump Pumps Vacuum Vacuum*
Hunter Alignment Rack Hunter Alignment Rack* Vacuum Pot Holing ( Excavating)
Vacuum Pot Holing
( excavating) *
Hydro Power Unit( s) Hydro Power Units Vertical Milling Machine Milling Machine
Hydropump Hydro Power Units Wacker
Trimmers/ Edgers/ Brush
Cutters
Ice- Machines Ice Machine* Water Boiler Boiler*
Industrial Tractor( s) Rubber Tired Loaders Water Extractor Water Extractor*
Irrigation Set( s) Irrigation Sets* Wave Rider Personal Water Craft
Jack Hammer Jack Hammer* Weed Eater
Trimmers/ Edgers/ Brush
Cutters
Jet Skies Personal Water Craft Weed Wacker
Trimmers/ Edgers/ Brush
Cutters
John Deere Unknown John Deere* Weed Whacker
Trimmers/ Edgers/ Brush
Cutters
Lawn Edger( s) Trimmers/ Edgers/ Brush Cutters Welder( s) Welders
Lawn Mower( s) ( Walk Behind) Lawn Mowers Well Well*
Lawn Trimmer( s) / Edger( s) Trimmers/ Edgers/ Brush Cutters Whacker
Trimmers/ Edgers/ Brush
Cutters
Lays Lathe* Wire Puller Electric*
Leaf Blower( s) ( Back Pack) Leaf Blowers/ Vacuums Wood Chipper Chippers/ Stump Grinders
Leaf Blower( s) ( Hand Held) Leaf Blowers/ Vacuums Woodsplitter Wood Splitters
Line Trimmer Trimmers/ Edgers/ Brush Cutters Yard Burn Yard Burn*
Loader( s) Rubber Tired Loaders Yard Truck Yard Truck*
Man Lift( s) Aerial Lifts Yard Vacuum Leaf Blowers/ Vacuums
Manual Milling Machine Manual* Zaper Saw Saw*
29
Respondent Equipment Types ARB Equipment Mapping Respondent Equipment Types ARB Equipment Mapping
Massey Ferguson Unknown Massey Ferguson*
Material Handling Equipment
( e. g., Conveyors, Rock Crushers) Materials Handling ( Other) *
* No exact ARB category match determined
30
Horsepower Assignments
In cases where the respondent did not provide a specific horsepower value for a piece of
equipment, horsepower assignments were made based on the following decision rules, presented
in order of precedence.
A. Where equipment make and model were provided, web searches were utilized to
find hp information when available.
B. Where a hp range was provided, the average of the minimum and maximum
horsepower range was used. Standard hp ranges provided to respondents
included:
· < 11;
· 11 – 24;
· 25 – 49;
· 50 – 74;
· 75 – 119; and
· 120 – 174.
Application Category Assignments
The survey included several standardized use categories including:
· Agricultural production and harvesting;
· Automotive;
· Building or construction;
· Industrial;
· Other ( e. g., cleaning or maintenance) – to be specified;
· Personal or residential;
· Recreational; and
· Warehousing.
In some instances when a respondent selected the “ Other” category, the additional description
provided by the respondent fit within one of the standardized uses originally presented to them.
In these instances, the use was changed from “ Other, specify” to the appropriate use from the
standardized list. The most common reassignments moved “ lawn care,” “ lawn maintenance,”
“ yard care,” and “ gardening” to the Personal/ Residential category.
Excluded Records
Some records were excluded from the data set based on answers indicating they were ineligible
for inclusion in the study. The number of non- electric records excluded from analyses, and on
what basis they were excluded, are summarized in Table 7.
31
Table 7. Basis and Count of Excluded Records
Reason for Exclusion # of Records
Zero Hours Operation 133
On- road Equipment 14
Outside hp Range 15
Manual Operation 3
Pneumatic Equipment 1
Refusal to Provide Equipment Info6 1
Total Records 167
Outlier/ Anomaly Identification
Some respondent answers for horsepower and/ or activity were identified as outliers, either too
high or too low, based on: horsepower ranges presented in ARB’s OFFROAD model, hp ranges
presented in EPA’s NONROAD2005 model,( 7) comparison with other respondent answers,
known acceptable fuel types for specific equipment types, or, in the case of activity, the number
of hours in a year. In consultation with ARB the contractor flagged suspect values for further
investigation. In these instances, the data collection subcontractor made an initial round of call-backs
to obtain clarification. Later, the contractor attempted to contact remaining respondents
for clarification. A summary of the second round of survey call- backs is presented in Table 8.
Table 8. Call Summary – Second Round Call- backs
Number of Respondents Identified for Call- backs 162
Number of Records with Outliers/ Anomalies 392
Number of Call- backs Attempted 119
No Answer 16
Left Message 51
Fax Number 3
Disconnected Number 4
Other Miscellaneous Responses 9
Number of Respondents without Contact Information 6
Number of Respondents Identified - Not Called* 39
Number of Records Updated 27
Number of Records Verified as Correct 19
* These represent records in the construction sector that had a seemingly low horsepower or activity upon initial QA.
After several phone calls to these types of outliers within this sector, it became apparent that these low numbers
were acceptable due to very limited use.
3.1.2 Survey Rates
As shown in Table 9, the combined results from the pilot and full- study totaled 1,164 completed
surveys, exceeding the study goal of 1,100.
6 Respondent indicating owning/ operating a piece of covered equipment but would not specify type or other data.
32
Table 9. Completed Questionnaires by Sample Type
Sample Type Target # of Completes Actual # of Completes Percent Actual
Agriculture 275 298 26%
Construction and Mining 225 246 21%
Residuals 275 293 25%
Residential 325 327 28%
Total 1,100 1,164 100%
Surveys that were completed over and above the expected number were the result of the mixed-mode
administration of the survey ( i. e., additional mail- in questionnaires were received after
telephone interviews were conducted).
In order to determine how the survey “ performed” for each sample type, disposition tables were
developed to provide results for all sample records identified for the pilot survey, as well as
assorted survey response parameters. Table 10 provides a description of the final dispositions
for all sample records that were used during the pilot and full- study surveys, by response sector.
Table 10. Final Dispositions for Final Off- road Sample
Agriculture Const/ Mining Residual Residential Total Survey Parameter
Count % Count % Count % Count % Count %
Sample Pieces Used 4,146 100% 5,785 100% 4,215 100% 9,404 100% 23,550 100%
Completed Surveys 298 7% 246 4% 293 7% 327 3% 1,164 5%
Eligible to Participate 385 9% 310 5% 377 9% 396 4% 1,468 6%
Ineligible to Participate 385 9% 1,001 17% 1,278 30% 1,257 13% 3,921 17%
Average Interview Length
( Phase I)
18.6 Minutes 13.6 Minutes 24.1 Minutes 11.6 Minutes -- --
Average Interview Length
( Phase II full study)
14.67 Minutes 11.3 Minutes 11.18 Minutes 9.83 Minutes -- --
Completes per Hour ( cph)
( Phase I)
0.19 CPH 0.24 CPH 0.27 CPH 0.34 CPH -- --
Completes per Hour ( cph)
( Phase II full study) 1.06 CPH 0.61 CPH 0.27 CPH 0.63 CPH -- --
The great majority of the sample was of unknown eligibility, meaning that either contact was
never made with that record or the call resulted in a callback or a soft refusal prior to eligibility
being determined. 7 Overall, once contact was made with an eligible equipment operator the vast
majority of operators went on to complete the survey ( 1,164 of 1,468). 8 A large number of
phone contacts were made with ineligible parties ( i. e., entities that did not own/ operate any off-road
equipment < 175 hp.) The incidence rate ( the ratio of ineligible to eligible respondents) was
7 A soft refusal is someone who initially says they won't participate in the survey. They are called back until they
make it clear they have no intention to participate.
8 Eligible respondents responded “ yes” to the questions: ( 1) do you own or lease at least one piece of off- road
equipment, and ( 2) does that equipment have a maximum horsepower rating of less than 175?
33
highest for the Agricultural Sector, at 50%. The incidence rates for the remaining three sectors
were all quite close, between 23% and 24%.
The differences in incidence rates are also reflected by the “ completes per hour” values shown in
Table 10. These data indicate a substantial increase in data collection efficiency for the full
study compared with the Phase I pilot.
3.1.3 Respondent Profiles
Profiles were developed to broadly characterize the survey respondents, in order to qualitatively
demonstrate broad representativeness of off- road equipment operators as a whole. Detailed
statistical analyses, including confidence intervals, are presented in Section 4 for each
equipment/ fuel type combination.
Because of the extreme variation within the agricultural industry ( e. g., types of crop, acreage
range), the agriculture sample was further broken down into six segments to ensure
representation within the industry’s multiple crops: Tree Fruit ( apricots, peaches, lemons, etc),
Row Crops, Nut Crops, and Other Crops ( including vineyards), Farm Management Companies
and CAFO/ Dairy. 9 For a complete listing of crop category assignments, see Appendix A.
Tables 11 thru 14 summarize the number of completes by respondent type within the
Agriculture, Construction and Mining, Residential, and Residual Sectors, respectively.
Completed surveys for the Agriculture sector in Table 11 are also reported by geographic area,
distinguishing respondents within the San Joaquin Valley ( SJV) from those in the rest of the
state. 10 SIC breakouts for the Construction and Residential sectors were selected to reflect
different equipment utilization patterns, based on contractor experience.
Table 11. Completed Surveys by SSI Crop/ Service Type – Agricultural Sector
Crop/ Service Type Completed Surveys
SJV Other Areas
Total Percentage
Tree Fruit 3 10 13 4%
Row Crop 38 42 80 27%
Nut Crop 49 13 62 21%
Other Crop 41 74 115 39%
Farm Management 8 4 12 4%
CAFO/ Dairy 2 14 16 5%
Total 141 157 298 100%
9 CAFO – Concentrated Animal Feeding Operations.
10 SJV consisting of Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties.
34
Table 12. Completed Surveys by SIC Group – Construction and Mining Sector
SIC Group Description SIC Total
Heavy- Highway 1611, 1622 13
Other Heavy Construction 1629 5
Utility 1623 2
Residential Buildings 1521, 1522, 1531 42
Other Buildings 1541, 1542 10
Special Trades - Excavation 1794 10
Special Trades - Other - all other 1700s ( less 1794) 149
Mining 1000s, 1200s, 1400s 15
Total 246
Table 12 indicates a predominance of respondents in the residential building and “ special trades
– other” category.
Table 13. Completed Surveys by Region – Residential Sector
Residence Area Total Percentage
Non Target 240 73%
Target 87 27%
Total 327 100%
Table 14. Completed Surveys by SIC Group – Residual Sector
SIC Group Description SIC Total
Division A - Non Agricultural
100s – 999, excluding 0711, 0721, 0722,
0762 ( Farm Mgmt.) 22
Manufacturing 2000 – 3999 75
Public Administration 9000 – 9999 3
Services 7000 – 8999 85
Transportation, Communications, Electric Gas and
Sanitary Services 4000 – 4999 17
Wholesale Trade 5000 - 5199 41
Retail Trade 5200 - 5999 50
Total 293
The respondents in the Residual sector were relatively dispersed across a wide range of SIC
groupings, although only a small number fell in the government category ( i. e., public
administration).
The respondent categories listed in Table 11 were obtained directly from SSI, the sample
provider for the Agricultural Sector. Eligible respondents were subsequently asked to categorize
their operations by crop type, as shown in Table 15. This crop type categorization, based on
stakeholder recommendations, provides slightly more detail than the SSI categories. In addition,
respondents reporting to provide Farm Management services ( 39 of the 298 completes) also
reported the crop type they typically service: citrus, one; CAFO/ dairy, two; nut, 10; row, 12;
other tree fruit, eight; and vineyards/ other, six.
35
Table 15. Completed Agricultural Surveys by Self- Reported Crop Type
Crop Type Completes - SJV Completes – Other Areas Total Completes
Tree Fruit ( non citrus) 18 36 54
Row Crop 26 36 62
Nut Crop 40 14 54
Vineyard/ Other Crop 29 42 71
Citrus 15 16 31
CAFO/ Dairy 13 13 26
Total 141 157 298
This study assumed the self- reported crop type provides a more accurate representation of
respondent operations than the sample frame categories, and was used for subsequent analyses.
Table 16 provides a detailed breakout of the acreage covered by county for the acreage covered
by the survey. The table also provides the total acreage in farms by county from the 2002
Agricultural Census ( 8). Survey coverage appears broadly representative of the state, with 55%
of surveyed acreage occurring within the SJV which contains 50% of the state’s agricultural
land.
Table 16. Completed Surveys and Associated Acreage by County – Ag. Sector
County Responses* Acreage*
Percent of
Survey
Acreage 2002
Census
Percent of
Census
Alameda 2 1,300 2.13% 10,608 0.07%
Alpine - 0 0.00% 850 0.01%
Amador - 0 0.00% 10,387 0.07%
Butte 3 2,735 4.48% 435,419 2.88%
Calaveras - 0 0.00% 4,796 0.03%
Colusa 1 300 0.49% 531,573 3.51%
Contra Costa 3 80 0.13% 41,933 0.28%
Del Norte - 0 0.00% 3,567 0.02%
El Dorado 7 211 0.35% 10,794 0.07%
Fresno^ 32 5,380 8.82% 1,869,960 12.36%
Glenn 14 1,320 2.16% 407,889 2.70%
Humboldt 1 58 0.10% 17,285 0.11%
Imperial 2 2,700 4.42% 725,045 4.79%
Inyo - 0 0.00% 3,805 0.03%
Kern^ 2 360 0.59% 1,327,926 8.77%
Kings^ 7 1,367 2.24% 364,399 2.41%
Lake - 0 0.00% 43,896 0.29%
Lassen - 0 0.00% 43,245 0.29%
Los Angeles 2 70 0.11% 38,756 0.26%
Madera^ 4 2,376 3.38% 512,209 3.38%
Marin - 0 0.00% 5,300 0.04%
Mariposa - 0 0.00% 761 0.01%
Mendocino 3 710 1.16% 54,911 0.36%
Merced^ 10 1,730 2.82% 699,471 4.62%
36
County Responses* Acreage*
Percent of
Survey
Acreage 2002
Census
Percent of
Census
Modoc 1 210 0.34% 113,848 0.75%
Mono - 0 0.00% 13,114 0.09%
Monterey - 0 0.00% 1,084,704 7.17%
Napa 7 610 1.00% 103,412 0.68%
Nevada - 0 0.00% 4,124 0.03%
Orange 3 667 1.09% 20,232 0.13%
Placer 1 > 1 0.00% 39,268 0.26%
Plumas - 0 0.00% 9,138 0.06%
Riverside 8 1,590 2.61% 385,915 2.55%
Sacramento 4 3,618 5.93% 187,224 1.24%
San Benito - 0 0.00% 103,670 0.68%
San Bernardino 8 239 0.39% 63,131 0.42%
San Diego 29 1,611 2.64% 180,460 1.19%
San Francisco - 0 0.00% 0 0.00%
San Joaquin^ 18 6,268 10.27% 916,279 6.05%
San Luis Obispo - 0 0.00% 228,282 1.51%
San Mateo - 0 0.00% 15,041 0.10%
Santa Barbara 5 1,200 1.97% 315,348 2.08%
Santa Clara 1 23 0.04% 47,010 0.31%
Santa Cruz - 0 0.00% 86,329 0.57%
Shasta 2 95 0.16% 22,740 0.15%
Sierra - 0 0.00% 2,800 0.02%
Siskiyou 1 500 0.82% 132,873 0.88%
Solano 2 1,020 1.67% 189,716 1.25%
Sonoma 5 1,324 2.17% 158,008 1.04%
Stanislaus^ 13 8,382 13.74% 640,572 4.23%
Sutter 5 416 0.68% 521,906 3.45%
Tehama 1 200 0.33% 126,471 0.84%
Trinity - 0 0.00% 932 0.01%
Tulare^ 42 9,076 14.87% 1,273,612 8.42%
Tuolumne 2 229 0.38% 1,094 0.01%
Ventura 14 2,244 3.68% 308,709 2.04%
Yolo 6 750 1.23% 514,551 3.40%
Yuba 1 75 0.12% 159,130 1.05%
Total 272 61,025 100.00% 15,134,428 100.00%
* Does not include responses or acreage from CAFO/ Dairy
^ SJV counties
Tables 17, 18, and 19 present the number of completed surveys by county for the Construction
and Mining, Residential, and Residual sectors, respectively.
37
Table 17. Completed Surveys by County – Construction and Mining Sector
County # Completes County # Completes
Alameda 6 Riverside 11
Butte 1 Sacramento 6
Calaveras 1 San Benito 1
Colusa 1 San Bernardino 13
Contra Costa 5 San Diego 12
El Dorado 3 San Francisco 2
Fresno 10 San Joaquin 8
Glenn 2 San Luis Obispo 8
Imperial 2 San Mateo 3
Inyo 1 Santa Barbara 3
Kern 7 Santa Clara 7
Kings 2 Santa Cruz 3
Los Angeles 40 Shasta 3
Madera 4 Siskiyou 4
Marin 3 Solano 1
Mendocino 3 Sonoma 8
Merced 1 Stanislaus 6
Monterey 5 Tehama 1
Napa 4 Tulare 5
Nevada 1 Tuolumne 1
Orange 21 Ventura 6
Placer 8 Yolo 3
Total 246
Table 18. Completed Surveys by County – Residential Sector
County # Completes County # Completes
Alameda 8 Placer 18
Amador 1 Riverside 15
Butte 7 Sacramento 5
Calaveras 1 San Bernardino 13
Colusa 1 San Diego 17
Contra Costa 11 San Joaquin 7
El Dorado 6 San Luis Obispo 5
Fresno 9 San Mateo 3
Glenn 1 Santa Barbara 6
Humboldt 4 Santa Clara 10
Imperial 11 Santa Cruz 6
Kern 9 Shasta 4
Kings 1 Siskiyou 2
Lake 61 Solano 3
Los Angeles 22 Sonoma 5
Marin 1 Stanislaus 6
Mendocino 1 Sutter 2
Merced 3 Tulare 6
38
County # Completes County # Completes
Monterey 7 Tuolumne 1
Napa 7 Ventura 4
Nevada 4 Yolo 3
Orange 9 Yuba 1
Total 327
Table 19. Completed Surveys by County – Residual Sector
County # Completes County # Completes
Alameda 9 Sacramento 14
Butte 1 San Bernardino 13
Calaveras 1 San Diego 19
Colusa 2 San Francisco 2
Contra Costa 5 San Joaquin 8
El Dorado 2 San Luis Obispo 4
Fresno 11 San Mateo 4
Glenn 2 Santa Barbara 4
Humboldt 2 Santa Clara 14
Imperial 2 Santa Cruz 5
Kern 7 Shasta 2
Kings 2 Sierra 1
Los Angeles 48 Siskiyou 3
Madera 1 Solano 6
Mariposa 1 Sonoma 8
Mendocino 9 Stanislaus 12
Merced 2 Tehama 3
Monterey 2 Trinity 2
Napa 1 Tulare 4
Nevada 1 Tuolumne 2
Orange 22 Ventura 9
Placer 4 Yolo 5
Riverside 11 Yuba 1
Total 293
Agriculture respondents other than CAFO/ Dairy were also asked to provide information on their
associated total acreage. The average acreage per farm for each crop type is provided in Table
20, with row crops having the largest average size and tree fruit the smallest.
Table 20. Agricultural Respondent Mean Acreage by Crop Type
Crop Type Mean Acreage Owned or Leased
SJV Other Areas
Nut Crop 340 186
Row Crop 192 266
Tree Fruit ( non- citrus) 90 144
Citrus 110 93
Vineyard/ Other 450 173
39
Tables 21, 22, 23, and 24 summarize the average, minimum, and maximum number of pieces of
equipment owned or operated by the respondents for each of the survey sectors. These summary
tables provide a general indication of the variability in fleet sizes for the different sectors.
Table 21. Agricultural Respondent Pieces of Equipment by Crop/ Service Type
Crop/ Service Type Number of Pieces of Equipment/ Respondent
SJV Other Areas
Avg. Min Max Variance Avg. Min Max Variance
Nut Crop 5.4 1 23 28.8 3.9 1 8 5.9
Row Crop 3.2 1 7 3.8 3.9 1 17 12.9
Tree Fruit ( non- citrus) 3.1 1 10 4.9 3.3 1 15 13.1
Citrus 3.3 1 11 6.8 3.3 1 9 8.2
Vineyard/ Other 8.2 1 65 151.0 4.1 1 19 23.4
CAFO/ Dairy 3.5 1 6 1.6 3.8 1 10 6.5
The variance of the distribution is also shown, indicating a relatively wide distribution across
fleet size for the vineyard/ other category in the SJV. Much of this variation is due to a single
respondent operating 65 pieces of equipment, with the next largest fleet consisting of only 25
units.
Table 22. Construction and Mining Respondent
Pieces of Equipment by Service Type
Service Type Average Min Max Variance
Construction 2.9 1 30 15.0
Mining 4.1 1 20 25.5
The construction and mining respondents show a somewhat wider distribution in fleet
sizes relative to most of the agricultural crop/ service type fleet.
Table 23. Residential Respondent Pieces of Equipment by Region
Respondent Area Average Min Max Variance
Non Target 2.2 1 14 3.4
Target 2.2 1 9 2.7
The residential sector exhibits the tightest distribution of the four survey sectors, as expected.
Table 24. Residual Respondent Pieces of Equipment by Service Type
Service Type Average Min Max Variance
Logging 6.2 1 23 47.2
Residual 2.9 1 130 70.6
40
Not surprisingly the residual sector shows the widest variance in fleet sizes of the four survey
sectors, likely due to the variety of SICs included in this sector.
3.1.4 Response Weightings
After the survey data had been quality assured and cleaned, analytic weights were developed to
reflect selection probabilities as well as to adjust for potential non- response bias. For example, it
is possible that businesses with larger equipment inventories may not participate at the same rate
as businesses that use little or no eligible equipment. Such differential non- response could bias
the results of the survey because the commercial distribution of surveyed off- road equipment
users would not represent the population distribution of businesses using off- road equipment. To
illustrate, if businesses with only one piece of eligible off- road equipment participated in the
survey at twice the rate as businesses with two or more pieces of eligible equipment, then the
estimated total pieces of equipment based only on the survey data ( i. e., without adjustment)
would understate the actual population total. For this reason analytic weights were developed to
correct for this type of bias for both the residential and commercial samples, as discussed below.
A total of 1,164 completed surveys of eligible respondents were collected. Table 25 summarizes
the distribution of these surveys across sample type. In this case Agricultural sample types refer
to SSI categorizations rather than self- reported crop types ( see Table 11).
Table 25. Distribution of Completed Surveys by Sample Type – Unweighted
Sample Type 1 Sample Type 2 Frequency
Agriculture Nut Crop 62
Agriculture Row Crop 80
Agriculture Tree Fruit 13
Agriculture Other 115
Agriculture Farm Management 12
Agriculture CAFO/ Dairy 16
Construction/ Mining Construction 231
Construction/ Mining Mining 15
Residual/ Logging Logging 13
Residual/ Logging Residual 280
Residential Target 87
Residential Non- target 240
Total 1,164
As discussed above, two separate sample frames were used for the selection of the commercial
( non- residential) sample data. The first source was an agriculture database maintained by SSI.
In addition to administrative data such as name, address and phone number, the full- coverage
nationwide database of farmers contains crop type and reported income from the sale of crops.
The second source was SSI’s B2B database, which contains a comprehensive list of nationwide
41
businesses based on the Dunn and Bradstreet SIC code database. 11 Table 26 identifies the
sample frame from which each commercial sample type was drawn.
Table 26. Commercial Surveys by Sample Type – Sample Frame
Sample Type 1 Sample Type 2 Frame
Agriculture Nut Crop Agriculture Database
Agriculture Row Crop Agriculture Database
Agriculture Tree Fruit Agriculture Database
Agriculture Other Agriculture Database
Agriculture Farm Management SIC Database
Agriculture CAFO/ Dairy Agriculture Database
Construction/ Mining Construction SIC Database
Construction/ Mining Mining SIC Database
Residual/ Logging Logging SIC Database
Residual/ Logging Residual SIC Database
Weights were created at the subsample level ( sample type 2) for the agricultural sector. Due to
the large number of completed surveys collected within the construction sector, and the wide
range of establishment types present ( and corresponding wide range of SIC codes), the
construction category was further stratified into three microstrata ( construction- a, construction- b,
construction- c). Similarly, the residual category was stratified into six microstrata ( residual- a
through residual- f). Each construction and residual microstratum represents a grouping of
similar establishment types ( based on SIC division and/ or major group). Table 27 provides a
detailed breakdown of corresponding SIC grouping by various levels of stratification.
Table 27. Sample Type, Sample Frame and Corresponding SIC Grouping –
Commercial Sectors
Sample Type 1 Sample Type 2 Microstrata Frame SIC Grouping
Agriculture Nut Crop N/ A Ag. Database Codes 0173, 0179 ( partial)
Agriculture Row Crop N/ A Ag. Database Industry Group 011, 013
Agriculture Tree Fruit N/ A Ag. Database Codes 0174, 0175, 0179 ( partial)
Agriculture Other N/ A Ag. Database Codes 0161, 0171, 0172, 0191
Agriculture Farm Management N/ A SIC Database Codes 0711, 0721, 0722, 0762
Agriculture CAFO/ Dairy N/ A SIC Database Industry Group 021, 024
Construction/ Mining Construction Construction- a SIC Database Major Group 15
Construction/ Mining Construction Construction- b SIC Database Major Group 16
Construction/ Mining Construction Construction- c SIC Database Major Group 17
Construction/ Mining Mining N/ A SIC Database Major Groups 10, 12, 14
Residual/ Logging Logging N/ A SIC Database Industry Group 241
Residual/ Logging Residual Residual- a SIC Database Division A - Non Ag
Residual/ Logging Residual Residual- b SIC Database Divisions D, E
Residual/ Logging Residual Residual- c SIC Database Division F
11 Dunn and Bradstreet is the industry standard for drawing samples of establishments for commercial surveys.
42
Sample Type 1 Sample Type 2 Microstrata Frame SIC Grouping
Residual/ Logging Residual Residual- d SIC Database Major Groups 52, 53, 54, 55, 57
Residual/ Logging Residual Residual- e SIC Database Major Groups 70, 75, 78, 79, 82, 84
Residual/ Logging Residual Residual- f SIC Database Major Groups 91, 92, 97
In broad terms, most of the Agricultural strata correspond to SIC Major Groups 01 ( Agricultural
Production Crops), and 02 ( Agricultural Production Livestock and Animal Specialties). The
Farm Management stratum corresponded largely to SIC Industry Groups 017 ( Soil Preparation
Services), 072 ( Crop Services), and 076 ( Farm Labor and Management Services). The
Construction and Mining strata correspond to SIC Division C ( Construction). The Logging
stratum corresponds to Industry Group 241 ( Logging). The remainder of the Residual strata
includes most/ all of SIC Division D ( Manufacturing), Division E ( Transportation,
Communications, Electric, Gas, and Sanitary Services), Division F ( Wholesale Trade), Division
G ( Retail Trade), and a targeted subset of Divisions I ( Services) and J ( Public Administration)
expected to utilize off- road equipment. SIC Division H ( Finance, Insurance and Real Estate)
was excluded from the sample frame selection, as little if any off- road equipment was expected
in this sector.
The detailed crop type assignment for the Agriculture sector is presented in Appendix A.
Appendix B lists the SIC groupings for each microstrata along with group descriptions.
Once the levels of stratification were established, the number of completed surveys, the total
number of eligible respondents, and the total number of records in the sample frame were
determined for each subsample type/ microstratum. These values were then used to calculate
proportions within each subsample type. Finally, the weights for each sample type ( sample type
2) were calculated by dividing the proportion of records in the frame by the proportion of
completed surveys, with the results shown in Table 28.12
Table 28. Relative Survey and Sample Size Proportions w/ Response Weightings
Sample Type 1 Sample Type 2 Microstrata
Completed
Surveys
Proportion
of
Completed
Surveys
Records
in
Frame
Proportion
of Records
in Frame Weight
Agriculture Nut Crop N/ A 62 0.208 1,830 0.134 0.644
Agriculture Row Crop N/ A 80 0.268 2,507 0.183 0.682
Agriculture Tree Fruit N/ A 13 0.044 3,568 0.261 5.983
Agriculture Other N/ A 115 0.386 3,835 0.281 0.728
Agriculture Farm Management N/ A 12 0.040 1,310 0.096 2.384
Agriculture CAFO/ Dairy N/ A 16 0.054 615 0.045 0.838
Subtotal. 298 13,665
Construction/ Mining Construction Construction- a 52 0.225 30,392 0.333 1.479
Construction/ Mining Construction Construction- b 20 0.087 4,235 0.046 0.531
12 Small adjustments were applied to these weights depending upon the analysis of interest, to account for missing
data fields. For example, when calculating average hp values within a sector, weights were recalculated as
described above, but using only those records for which hp data were available.
43
Sample Type 1 Sample Type 2 Microstrata
Completed
Surveys
Proportion
of
Completed
Surveys
Records
in
Frame
Proportion
of Records
in Frame Weight
Construction/ Mining Construction Construction- c 159 0.688 56,575 0.620 0.901
Subtotal. 231 91,202
Construction/ Mining Mining N/ A 15 1 406 1 1.000
Residual/ Logging Logging N/ A 13 1 274 1 1.000
Residual/ Logging Residual Residual- a 22 0.079 32,482 0.085 1.082
Residual/ Logging Residual Residual- b 79 0.282 115,907 0.302 1.070
Residual/ Logging Residual Residual- c 41 0.146 75,341 0.196 1.339
Residual/ Logging Residual Residual- d 50 0.179 66,706 0.174 0.974
Residual/ Logging Residual Residual- e 85 0.304 90,177 0.235 0.774
Residual/ Logging Residual Residual- f 3 0.011 3,426 0.009 0.840
Subtotal. 280 384,039
Residential Target N/ A 87 0.169 - 0.0337* 0.127
Residential Other Residential N/ A 240 0.831 - 0.9663* 1.317
Subtotal . 327 -
Total 1,164 489,586
Note: The proportions for each shaded/ non- shaded region sum to 1.
* Residential proportions derived from relative number of households in Target and Other Residential area counties.
These weights were applied to the data when conducting analyses at the sector level. Table 29
provides the resulting weighted frequency distribution by sample type.
Table 29. Weighted Survey Response Totals
Sample Type 1 Sample Type 2 Microstrata Final Weight Completed Surveys - Weighted
Agriculture Nut Crop N/ A 0.644 40
Agriculture Row Crop N/ A 0.682 55
Agriculture Tree Fruit N/ A 5.983 78
Agriculture Other N/ A 0.728 84
Agriculture Farm Management N/ A 2.384 29
Agriculture CAFO/ Dairy N/ A 0.838 13
Construction/ Mining Construction a 1.479 77
Construction/ Mining Construction b 0.531 11
Construction/ Mining Construction c 0.901 143
Construction/ Mining Mining N/ A 1 15
Residual/ Logging Logging N/ A 1 13
Residual/ Logging Residual a 1.082 24
Residual/ Logging Residual b 1.070 85
Residual/ Logging Residual c 1.339 55
Residual/ Logging Residual d 0.974 49
Residual/ Logging Residual e 0.774 66
Residual/ Logging Residual f 0.840 3
Residential Target N/ A 0.127 11
Residential Other Residential N/ A 1.317 316
Total 1,164*
* Summation ( 1,167) difference due to rounding error
44
3.1.5 Equipment Inventory Findings
The following provides descriptive statistics for a variety of survey parameters, including
equipment and fuel type distributions, activity profiles and application types, and hp and model
year distributions. The analysis excludes electric equipment from all but the equipment type
distribution analysis. These profiles are provided at the sector level – a detailed statistical
analysis is provided for the statewide equipment population as a whole in Section 4.
Equipment Type Distributions
Weighted equipment counts were tallied for each equipment type identified by survey
respondents. For this summary, equipment types are not differentiated by fuel or application
type. For example, lawn mowers are reported in the Agricultural Sector totals, although this
equipment was almost exclusively designated as “ personal/ residential” use. Fuel type and
application distributions are discussed separately below, and in more detail in the Preemption
Analysis in Section 4.
The reported equipment type distribution within the Agricultural sector is presented in Figure 7.
Forty two separate equipment types were reported altogether, for a total weighted equipment
count of 1,183. Note that agricultural tractors were by far the most common piece of equipment
reported, and are not presented in the figure due to scale considerations. Of the remaining
equipment types, ATVs were the next most prevalent, followed closely by sprayers. Although
with substantially lower totals, industrial equipment such as forklifts, construction equipment
such as rubber tire loaders and tractor/ loader/ backhoes, and lawn and garden equipment such as
trimmers and lawn mowers are fairly common as well. The Miscellaneous category included a
wide variety of equipment types, none of which totaled more than three observations. These
included generators sets, balancers, and tillers, among others, with 18 individual equipment
categories included in all. The majority of the remaining units consisted of a number of specialty
agricultural equipment. Miscellaneous equipment categories in this sector are listed below,
along with their weighted population counts.
· Generator sets ( 3)
· Cranes ( 3)
· Tillers ( 3)
· Balancers ( 3)
· Yard trucks ( 2)
· Chainsaws ( 1)
· Trenchers ( 1)
· Welders ( 1)
· Excavators ( 1)
· Ag wells ( 1)
· Bale haulers ( 1)
· Crawler tractors ( 1)
· Skid steer loader ( 1)
· Aerial lifts ( 1)
· Leaf blower/ vacuums ( 1)
· Shredders ( 1)
· Unknown “ Caterpillar” ( 1)
· “ Diesel Motor” ( 1)
45
Figure 7. Agricultural Sector Population Distribution ( w/ out tractors)*
72
60
28 27
22
19 16
12 12 11 10 10 9 8 7 7 6 6 4 1
0
10
20
30
40
50
60
70
80
ATVs
Sprayers
Misc. Equipment
Forklifts
Ag Sweeper
Harvesters
Balers
Rubber Tired Loaders
Agricultural Mowers
Trimmers/ Edgers/ Brush Cutters
Electric
Spreader
Tractors/ Loaders/ Backhoes
Shaker
Swathers
Wood Splitters
Front/ Riding Mowers
Lawn Mowers
Pumps
Irrigation Sets
Equipment Type
Weighted Survey Counts
* 837 ag tractors
N = 1,183 weighted units
46
The low number of pumps and irrigation sets reported in this sector was unexpected and may be
indicative of under- reporting on the part of survey respondents rather than actual low population
counts. Specifically, we suspect that respondents may not have considered these equipment
types to be “ off- road” even though agricultural pumps were explicitly included in the list of
example equipment for this sector.
Figure 8 presents the weighted distribution of equipment types reported within the Construction
and Mining sector. A broad range of reported equipment types are included, covering 42
categories, for a total of 641 weighted pieces of equipment. Electric equipment was by far the
most common category at 188 pieces, and is excluded from the chart due to scale. Of the
remaining equipment types, generator sets, air compressors, and tractor/ loader/ backhoes are
ubiquitous within this sector. Although substantially less common, skid steer loaders and
industrial forklifts are the next most common types.
Heavier pieces of equipment such as excavators and crawler tractors/ dozers are much less
common in the Construction and Mining sector, perhaps because units less than 175 hp are
relatively uncommon for these categories. The most common construction equipment categories
are represented to some degree however, with the exception of rough terrain forklifts and
surfacing equipment. Thirteen equipment categories were included in the Miscellaneous
category, with none having greater than five observations. These included assorted lawn and
garden equipment, unspecified vacuums, and various specialty equipment ( e. g., pipe threaders).
Miscellaneous equipment categories in this sector are listed below, along with their weighted
population counts.
· Vacuums ( 5)
· Trimmers/ edgers/ brushcutters ( 3)
· Snowmobiles ( 3)
· Pipe threaders ( 2)
· Leaf blowers/ vacuums ( 2)
· Champ ( 1)
· Hydro power units ( 1)
· Tillers ( 1)
· Vessels w/ outboard engines ( 1)
· Storm grinders (< 1)
· Chippers/ stump grinders (< 1)
· Material handling - other (< 1)
· Water truck (< 1)
Figure 9 summarizes the equipment distribution reported for the Residential sector. This sector
reported the lowest number of discrete equipment categories with 27. The total weighted
equipment count for this sector came to 704 units. Lawn mowers, electric equipment,
trimmers/ edgers/ brushcutters, and chainsaws were pervasive within this sector. Perhaps
unexpected, agricultural tractors were reported with some frequency. Alternatively, certain types
of recreational equipment were reported only infrequently ( e. g., personal watercraft and
minibikes). Miscellaneous equipment categories in this sector are listed below, along with their
weighted population counts.
· “ Yard burn” ( 1)
· Snowblowers ( 1)
· Cement & mortar mixers (< 1)
· “ Dirt remover” (< 1)
· Graders (< 1)
· Snowmobiles (< 1)
· Sprayers (< 1)
47
Figure 8. Construction and Mining Sector Population Distribution ( w/ out Electric Equipment*)
86 84 81
29
21 18 17 17 16
12 11 10
0
10
20
30
40
50
60
70
80
90
100
Generator Sets
Air Compressors
Tractors/ Loaders/ Backhoes
Skid Steer Loaders
Forklifts
Misc Equipment
Pressure Washers
Rubber Tired Loaders
Rollers
Bore/ Drill Rigs
Excavators
Sprayers
Equipment Type
N = 641 weighted units
* 188 electric pcs
48
Figure 8. Construction and Mining Sector Population Distribution Continued
8
5
5 5 5 4
4 4
3
2
1 1 1
1 1
0
1
2
3
4
5
6
7
8
9
Pumps
Graders
Concrete/ Industrial Saws
Front Mowers
Crawler Tractors
Cement and Mortar Mixers
Aerial Lifts
Welders
Cranes
Paving Equipment
Scrapers
Signal Boards
Trenchers
Pavers
Plate Comactor
Equipment Type
Weighted Survey Count
49
Figure 9. Residential Sector Equipment Population Distribution
245
144
90
71
33 26 19 16 13 10
0
40
80
120
160
200
240
280
Lawn Mowers
Electric
Trimmers/ Edgers/ Brush Cutters
Chainsaws
Leaf Blowers/ Vacuums
Front/ Riding Mowers
Off- Road Motorcycles
Agricultural Tractors
Tillers
ATVs
Equipment Type
Weighted Survey Count
N = 704 weighted units
50
Figure 9. Residential Sector Equipment Population Distribution Continued
5
5 4
4 4
3
3
3 3
1
0
1
2
3
4
5
6
Pressure Washers
Vessels w/ Outboard Engines
Generator Sets
Chippers/ Stump Grinders
Personal Water Craft
Misc equipment
Shredders
Golf Carts
Specialty Vehicles Carts
Minibikes
Equipment Type
Weighted Survey Count
51
Figure 10 presents the equipment distribution for the Residual sector. This sector reported the
greatest number of equipment types at 48, with 860 weighted units. This finding is not
surprising since this sector covers the broadest range of applications ( commercial, other than
agricultural and construction/ mining).
Electric equipment is by far the most common, followed by industrial forklifts. The high number
of transportation refrigeration units ( TRUs) appears to be an anomalous result, with all units
being reported by a single respondent – no other TRUs were reported among any other
respondent in any sector.
The remainder of the reported categories in the Residual sector consisted largely of various
agricultural, construction, and lawn and garden equipment. The Miscellaneous category
consisted of a very wide range of equipment types ( 31 total), with none having more than 3
observations. The following equipment types were included in the Miscellaneous category for
this sector, along with their weighted populations.
· Car lift ( 3)
· Pressure washer ( 3)
· Golf cart ( 3)
· Welder ( 2)
· Chipper/ Stump grinder ( 2)
· Skid steer loader ( 2)
· Personal watercraft ( 2)
· Lawn mower ( 2)
· Splice ( 1)
· Ag sweeper ( 1)
· Cart ( 1)
· “ Feed Feeder” ( 1)
· Sprayer ( 1)
· Sweeper/ Scrubber ( 1)
· Tamper/ Rammer ( 1)
· Thatcher ( 1)
· Trencher ( 1)
· Chainsaw ( 1)
· Vacuum pot holer ( 1)
· Agricultural tractor ( 1)
· Front/ Riding mower ( 1)
· Aerial lift ( 1)
· Alignment rack ( 1)
· Minibike ( 1)
· Snowblower ( 1)
· Tire balancer ( 1)
· Tire changer ( 1)
· Skidder (< 1)
· Crawler (< 1)
· Excavator (< 1)
· Grader (< 1)
While this sector reported a very diverse range of equipment categories, several specialty pieces
of equipment were not identified ( e. g., ground support equipment, or “ GSE”), due to the overall
rarity of such equipment, and the limited sample size in this sector.
A geographic breakdown was also prepared for the Agricultural sector, differentiating between
equipment operated in the San Joaquin Valley ( SJV) and other areas of the state. Table 30
summarizes the non- electric equipment categories and weighted equipment counts for all
equipment reported by Agricultural sector respondents, broken out by production region. ( Note
that all equipment and fuel type data presented in this and subsequent tables refer to non- electric
equipment, unless otherwise noted.)
52
Figure 10. Residual Sector Equipment Population Distribution
283
192
145
46 42
25 20 19 15 13 12 11 10 10 6 6 6
0
50
100
150
200
250
300
Electric
Forklifts
Transport Refrigeration Units
Agricultural Tractors
Misc. Equipment
Tractors/ Loaders/ Backhoes
Generator Sets
Trimmers/ Edgers/ Brush Cutters
Front/ Riding Mowers
Rubber Tired Loaders
Chainsaws
Agricultural Mowers
Air Compressors
ATVs
Pumps
Tillers
Leaf Blowers/ Vacuums
Equipment Type
Weighted Survey Count
N = 860 weighted units
53
Table 30. Equipment Categories and Counts Reported by Agricultural Region
Region Reported Equipment Categories Weighted Equipment Count
SJV 26 639
Other Areas 31 534
Total 42 1,173
Fuel Type Distributions
Fuel type was specified for all but 35 pieces of equipment (~ 1% of non- electric equipment
records). Fuel type assignments for these units were made allocating them proportionally among
other units in the same equipment category. Fuel type distributions were calculated for the
weighted equipment counts, by survey sector. Percentages are provided for gasoline, diesel, and
compressed gas ( including LPG and natural gas). All equipment categories are presented,
regardless of the number of observations - a formal uncertainty analysis is performed for unique
equipment/ fuel type combination in Section 4.
Table 31 presents the weighted fuel type distributions for the Agricultural sector. Notably, 94%
of agricultural tractors were diesel powered, with the remainder powered by gasoline. Similarly,
most traditional agricultural equipment was predominantly diesel, including balers, combines,
shakers, and swathers. Notable exceptions include agricultural mowers and sprayers, which are
predominately gasoline powered. Gasoline engines were also predominant among lawn and
garden equipment and generator sets. The majority of industrial forklifts were powered by
compressed gas ( specifically LPG), although significant numbers were also powered by gasoline
and diesel as well. Some unusual equipment/ fuel type combinations are also seen, including
compressed gas spreaders and welders, although these distributions are likely not representative
of the equipment population as a whole given the low observation count for these pieces.
Table 31. Weighted Fuel Type Distribution – Agricultural Sector
Equipment Type Weighted Count Compressed Gas Diesel Gasoline
Aerial Lifts 1 0% 0% 100%
Ag Wells 1 0% 100% 0%
Ag Sweeper 22 0% 94% 6%
Agricultural Mowers 12 0% 29% 71%
Agricultural Tractors 836 0% 94% 6%
All Terrain Vehicles 72 0% 10% 90%
Balancers 3 0% 100% 0