1 Draft Regulatory Impact Analysis, EPA420 R 03 008, April 2003. See www. epa. gov/ nonroad/.

dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 MEMORANDUM SUBJECT: Population Growth Projections for Nonroad Diesel Equipment FROM: NONROAD Emissions Modeling Team Assessment and Standards Division, Office of Transportation and Air Quality TO: Environmental Protection Agency ( EPA), Air Docket A 2001 28; E DOCKET OAR 2003 0012 DATE: April 23, 2004 1. Background EPA presented diesel fuel consumption projections in Chapter 3 ( Table 3.1 8) of the Draft Regulatory Impact Analysis ( DRIA) associated with the proposed rule " Control of Emissions of Air Pollution From Nonroad Diesel Engines and Fuel," ( 68 FR 28327, May 23, 2003). 1 These fuel consumption projections were also implicit in the pollutant benefits projected, in that nonroad diesel equipment only emit exhaust pollutants during operation. More or less fuel consumption inherently means more or less emissions, other factors being equal.

During the public comment process for the proposed rule, several commenters raised questions concerning the equipment population growth projections used in EPA's inventory modeling, which are higher than broadly analogous diesel fuel consumption projections published by the Energy Information Administration ( EIA). To address these comments EPA investigated many of the important parameters upon which the growth estimates in Draft NONROAD2004 were based.

This memo describes the key analyses designed to investigate the EPA growth estimates, particularly the relationships among equipment sales, expected equipment life, scrappage, and equipment populations. In the end, on the basis of methods and assumptions similar to those used in the NONROAD model, we were unable to reasonably reconcile the relatively high growth in equipment populations and work output projected in our analyses with EIA's growth projections for diesel fuel consumption. Thus, the draft NONROAD2004 model has retained its current dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 2 equipment population growth projections. However, for purposes of the final rule, the Final Regulatory Impact Analysis includes a sensitivity analysis in which fuel consumption and emissions projections from Draft NONROAD2004 are adjusted to match corresponding fuel consumption projections derived from EIA sources. This analysis will allow an assessment of the appropriateness of the new standards under the assumption that the EIA based projections were correct.

These analyses covered the four largest diesel equipment segments in the NONROAD model, which would account for the bulk of nonroad diesel fuel use. These four segments are: agricultural ( e. g., farm tractors, combines, etc.), construction ( e. g., earth moving equipment), commercial ( e. g., generator sets, pumps, etc.), and industrial ( e. g., mobile refrigeration, forklifts, sweepers, etc.).

2. Sales Growth Projection Methods The Data. These analyses were performed using diesel equipment sales histories obtained from the commercially available OELink   database, produced by Power Systems Research/ Compass International, Inc., ( PSR). The database contains estimates of annual sales for a wide variety of equipment types produced and sold into the U. S. market, over a 27 year period ( 1973 2000). Before examination and analysis of trends, we summed the data by equipment type and size class, where engine size is defined in terms of rated horsepower. Sixteen of the 18 engine size classes used in NONROAD and relevant to this analysis are presented in Table 1.

Approach. Because the NONROAD model projects growth in equipment populations, and because the available data represent historical equipment sales, we elected to estimate population growth from sales growth by means of a sales and scrappage model. The first step in this process was to derive sales growth rates from historical sales trends, for specific combinations of equipment type and size class. The approach employed was to fit trends to time series representing equipment sales histories, and to extrapolate the derived trends over the forecast horizon of the NONROAD model ( 2000 2050).

The Analysis. To derive sales growth rates, the following steps were performed:

 The time series for each equipment type and size class was plotted and examined.

 Decisions were made concerning the approach to curve fitting.  Within some agricultural equipment types, size classes were combined to increase sample sizes.  For each of the other three equipment categories all equipment types of that category were combined by size class.  Visual evaluation also governed the selection of the form of the trend to be fit.

 A trend was fit, with the optimal fit determined by minimization of least squares. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 3 Trend Forms. The trends fit to the data were selected to follow linear, exponential or logistic forms. Visual examples of these trends are shown in Figure 1. The form for the linear fit is where y t is the projected sales growth in year t, and c and m are the intercept and slope coefficients, respectively. When expressed in percentage terms relative to each prior year's population, the resulting annual growth rate declines gradually over time.

The form for the exponential fit is Unlike the linear trend, the percentage growth rate in each year relative to the preceding year for the exponential trend remains constant over time, but relative to a single year the percentage growth increases over time for a positive trend or decreases for a negative trend. For declining sales trends, the exponential trend has the advantage that the predicted sales can never go negative.

As a third alternative, logistic trends were fit to six time series ( four agricultural and two construction) showing rapid growth during the last one third to one half of the sales history. This approach is consistent with approaches currently used to model phenomena such as the introduction of new products to market. The logistic function is described using three to four parameters, as where K, A and L are empirically derived coefficients and   is an exponential growth rate, also empirically derived. The logistic function describes a characteristic sigmoid or " s shaped" curve, in which projected sales follow exponential growth patterns between the lower bound L and the upper bound K. y c mt t = + ( 1)

log ( ) y c mt y t t c mt = + = + or 10 ( 2)

y K Ae L t t = + +   1   ( 3) dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 4 Example Trend Fits 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 Year Relative Sales per Year Linear Exponential Logistic Figure 1. General examples of linear, negative exponential and logistic trends.

2.1 Agricultural Equipment Sales Projection Methods An initial analysis focused on equipment types that the NONROAD model classifies as " agricultural engines," since these engines constitute a substantial portion of the nonroad diesel equipment population, fuel consumption, and thus emissions. Selected equipment types in this category are listed in Table 2. The sales growth analysis for agricultural equipment was performed under contract by Research Triangle Institute ( RTI). The analysis is further described in a memo drafted by RTI to summarize their work, which is appended to this document as Appendix A.

Within each agricultural equipment type, engines were further classified by size, as presented above ( Table 1). However, during the analysis, specific size classes used for each equipment type were aggregated as necessary to provide adequate sample sizes for each time series.

Truncation of Time Series. Prior to curve fitting, some time series were truncated to remove the effects of a serious macroeconomic shock in the equipment market. Specifically, the 1970s boom in grain sales to the Soviet Union attracted many new entrants into farm operations. Subsequently, the embargo imposed on grain sales to the Soviet Union in 1980 led to a crash in new equipment sales over the ensuing six to 10 years. However, the presence of such an event in the time series could not be considered typical, and would have an undue influence on a time series intended for projection over several decades. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 5 Linear fits were used for three agricultural time series that showed steady growth over the sales history. Exponential trends were fit to two time series showing very slow growth and 14 series showing sales declines. Logistic trends were fit to four time series showing rapid growth during the last one third to one half of the sales history. Table 3 below lists which trend forms applied to specific equipment types/ size class combinations. The specific time period used to derive each trend is also indicated.

Using the sales trend fits described above, absolute sales were projected into the future for each size class in each of the seven agricultural equipment types examined. Because these sales projections were derived using variable size classes ( Table 3), they could not be used directly to project sales for NONROAD size classes within equipment types, unless the size class used for trend fitting exactly matched that used for projection. For example, regression coefficients derived for 50 75 hp tractors can be applied directly to project sales for this size class. However, the coefficients derived for 175+ hp tractors cannot be used directly to project sales for 300 600 hp tractors without erroneous results. Therefore, to adapt the broad level sales projections to narrower size classes within individual equipment types, the projections were converted from absolute sales to " relative sales." In the case of linear trend fits, absolute sales were expressed as a single annual percentage increase relative to the 1996 sales data point ( g linPct), as in equation ( 4) below,

where m, c and S 1996 are the regression slope term ( engines/ year), intercept ( engines) and projected sales in 1996 for a given combination of equipment type and size class ( e. g., tractors, 50 75 hp), respectively. Averaging annual sales over a specific time period and using the year 1996 as a fixed reference point express the linear growth in relative terms as a constant value over the projection period.

In the case of exponential fits, the resulting percentage growth rate was applied directly in all years, since a constant growth rate in percentage terms is a defining characteristic of an exponential trend.

In the case of logistic fits, the absolute sales projections were expressed as fractional changes in each year relative to the preceding year ( g log%, y), starting with the year 1996,

where S y and S y 1 are projected sales in years y and y 1, respectively. g m S m c m linPct= = + 1996 1996 ( 4)

g S S S y y y y log%, =

  1 1 100 ( 5) dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 6 2.2 Construction, Commercial, and Industrial Equipment Sales Projection Methods Subsequent analyses focused on equipment types classified by NONROAD as belonging to " construction," " industrial," and " commercial" segments. As with agricultural engines, we fit trends to sales histories. The treatment of engines in these three segments differs with respect to the level of detail. Before examining sales trends, we summed sales for all equipment types in each segment ( construction equipment, etc.) by NONROAD size classes, rather than for individual size classes within equipment types. All the trend fits were linear, except for two size classes for construction engines ( 75 100 hp, 100 175 hp). Due to the strong growth seen in these classes during the 1990' s, we projected sales growth using logistic, rather than linear fits. For most trends modeled, the entire sales history from 1973 to 2000 was modeled, except for commercial equipment, for which we modeled the period 1973 1997. For commercial engines, we omitted sales during 1998 2000, which showed a sharp spike in sales of small generators, apparently due to concerns related to Y2K. The forms of the equations are the same as those used for agricultural engines, as described above.

Using the sales trend fits described above, absolute sales were projected into the future for each size class in each of the three industry segments examined ( construction, industrial, commercial). Because these sales projections were for broad groupings including multiple equipment types, they could not be used directly to project sales for individual equipment types or size classes within equipment types. For example, if the slope coefficient for 16 25 hp construction equipment were 10,000 engines per year, this value could not be directly applied to 16 25 hp trenchers ( which have a total population of several hundred), without erroneous results. Therefore, to adapt the broad segment level sales projections to individual equipment types, the projections were converted from absolute sales to " relative sales." In the case of linear trend fits, absolute sales were expressed as a single annual percentage increase relative to the 2000 sales data point ( or 1997 for commercial equipment) ( g linPct), as in equation ( 4) above.

In the cases of the two size classes of construction engines for which logistic fits were used, different relative growths were needed for each future year. Thus, for these trends, the absolute sales projections for each year were converted to ratios to the year 1996 sales data point ( r log, y).

These relative growth values ( linear or logistic) by industry segment and power size class were then applied to each specific equipment type within the corresponding segment and size class to generate projected sales for each equipment type and size class. Section 3.2 below details the trends applied to each equipment type. r S S y y log, = 1996 ( 6) dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 7 Table 1 Diesel Equipment Size Classes Represented in the Draft NONROAD2004 Model ( rated horsepower)

3 < hp

6 6 < hp

11 11 < hp

16 16 < hp

25 25 < hp

40 40 < hp

50 50 < hp

75 75 < hp

100 100 < hp

175 175 < hp

300 300 < hp

600 600 < hp

750 750 < hp <= 1000 1000 < hp <= 1500 1500 < hp <= 2000 2000 < hp <= 3000 dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 8 Table 2 Important Agricultural Equipment Types Represented in the NONROAD Model Agricultural Tractors Balers Combines Irrigation Sets Sprayers Windrowers Other Agricultural Equipment 3. Sales Growth Trend Fit Results Results of sales trend fitting are presented below, first for agricultural equipment and then for the other three equipment segments considered in this analysis.

3.1 Agricultural Equipment Sales Trend Projections For each combination of equipment type and size class, Table 3 shows the time period modeled and the type of fit. Tables 4 6 show the trend fit coefficients and the corresponding growth rates.

3.2 Construction, Commercial, Industrial Equipment Sales Trend Fit Results Tables 7 9 present linear regression coefficients and equivalent annual percentage sales growth rates by NONROAD size class for construction, industrial and commercial engines, respectively. Table 10 presents parameters for the two size classes of construction engines for which sales were projected using logistic trends. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 9 Table 3 Agricultural Equipment Sales Trend Types Application Engine Size Class ( rated hp) Time Period Type of Fit Agricultural Tractors 11 25 1980 2000 Logistic Agricultural Tractors 25 50 1973 2000 Logistic Agricultural Tractors 51 75 1973 2000 Exponential Agricultural Tractors 75 100 1973 2000 Exponential Agricultural Tractors 100 175 1982 2000 Linear Agricultural Tractors 175 + 1982 2000 Linear Combines 75 100 1973 2000 None Combines 100 175 1982 2000 Exponential Combines 175 + 1982 2000 Linear Windrowers all 1977 2000 Exponential Bakers all 1982 2000 Exponential Sprayers 11 100 1977 2000 Exponential Sprayers 100 + 1973 2000 Logistic Irrigation Sets 11 25 1973 2000 Exponential Irrigation Sets 25 50 1973 2000 Exponential Irrigation Sets 50 75 1973 2000 Exponential Irrigation Sets 75 100 1973 2000 Exponential Irrigation Sets 100 175 1973 2000 Exponential Irrigation Sets 175 + 1973 2000 Exponential Other Ag Equipment 11 50 1973 2000 Exponential Other Ag Equipment 50 75 1973 2000 Exponential Other Ag Equipment 75 100 1977 2000 Exponential Other Ag Equipment 100 175 1989 2000 Exponential Other Ag Equipment 175 300 1989 2000 Exponential Other Ag Equipment 300 + 1989 2000 Logistic dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 10 Table 4 Fit Parameters and Annual Growth Rates for Agricultural Equipment with Linear Sales Trends1 Application Size Class ( rated hp) intercept ( c ) slope ( m) Linear Sales Growth Rate (% ) 2 Agricultural Tractors 100 175  1,433,278 727.82 3.74%

Agricultural Tractors 175 +  1,726,573 877.7807 3.45%

Combines 175 +  176,357 91.3632 0.03%

1 See Equation 1. Time t is entered into the linear equation as calendar year, e. g., " 2010." 2 Calculated in relative terms as the constant linear rate relative to 1996 year sales , i. e., glinPct( See Equation 4).

Table 5 Fit Parameters and Annual Growth Rates for Agricultural Equipment with Logistic Sales Trends1 Application Size Class ( rated hp) Upper bound ( K) Linear coefficient ( A) Growth Rate (  ) Lower Bound ( L) Annual Sales Growth Rate (%

2001 10 2011 20 2021 30 2031 Agricultural Tractors 11 25 68,000 200 0.1337 0 8.01% 4.22% 1.56% 0.4 Agricultural Tractors 25 50 75,000 9 0.0900 0 2.37% 1.17% 0.52% 0.2 Sprayers 100 + 4,310 7,000 0.4190 90 0.89% 0.03% 0.00% 0.0 Other Ag Equipment 300 + 560 27 0.612 0 0.31% 0.00% 0.00% 0.0 1 See Equation 3. Time t is entered into the logistic equation as ( t  1970), i. e., " 1970" is treated as t= 0. 2 Calculated as fraction change from year to year ( glog%, y), as in Equation ( 5). Values presented are averages for the periods indicate dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 11 Table 6 Fit Parameters and Annual Growth Rates for Agricultural Equipment with Exponential Sales Trends1 Application Size Class ( rated hp) intercept ( c ) slope ( m) Annual Sales Growth Rate (%/ year)

Agricultural Tractors 50 75 7.9087  0.001921  0.44%

Agricultural Tractors 75 100 29.0167  0.01255  2.9%

Combines 100 175 129.2412  0.06327  14%

Windrowers all 22.5105  0.009609  2.2%

Balers all  2.7593 0.002562 0.59%

Sprayers 10 100 9.7729  0.00367 0.84%

Irrigation Sets 11 25 49.67554  0.02415  5.4%

Irrigation Sets 25 50 99.9526  0.04919  11%

Irrigation Sets 50 75 99.9796  0.04925  11%

Irrigation Sets 75 100 3.0417 0.00006454 0.015%

Irrigation Sets 100 175 17.8374  0.007347  1.7%

Irrigation Sets 175 + 4.5922  0.0009645  0.22%

Other Ag Equipment 11 50 20.7211  0.009219  2.1%

Other Ag Equipment 50 75 116.07813  0.05758  12%

Other Ag Equipment 75 100 14.8355  0.006342  1.4%

Other Ag Equipment 100 175 27.9645  0.1262  2.9%

Other Ag Equipment 175 300 5.8907  0.001526  0.35%

1 See Equation 2. Time t is entered into the exponential equation as calendar year, e. g., " 2010." dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 12 Table 7 Fit Parameters and Annual Growth Rates for Linear Equipment Sales Trends for Construction Engines1 Segment Size Class ( rated hp) 2 intercept ( c ) slope ( m) Linear Sales Growth Rate (% of calculated 1996 sales) 3 Construction 3 6  340.0 128.1 4.91%

6 11 415.6 79.5 3.54%

11 16 237.2 123.3 4.01%

16 25  2636.5 601.4 5.37%

25 40  905.9 775.8 4.58%

40 50  347.4 385.6 4.53%

50 75 13999.8 354.9 1.60%

75 100 Logistic fit used, see Table 10 100 175 Logistic fit used, see Table 10 175 300 9351.2 586.3 2.57%

300 600 4302.0 233.9 2.42%

600 750  21.3 87.7 4.39%

750 1,000 198.9 19.6 3.02%

1,000 1,500  114.6 23.4 5.53%

1,500 2,000  118.1 21.5 5.71%

2,000 3,000  22.8 4.8 5.49%

1 See Equation 1. Time t is entered into the linear equation as ( t  1973), i. e., " 1973" is treated as t= 0, unlike the agricultural linear fit. 2 Defined as lower bound

rated power < upper bound. 3 Calculated in relative terms as the constant linear rate relative to 1996 year sales , i. e., glinPct ( See Equation 4). dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 13 Table 8 Fit Parameters and Annual Growth Rates for Linear Equipment Sales Trends for Commercial Engines1 Segment Size Class ( rated hp) 2 intercept ( c ) slope ( m) Linear Sales Growth Rate (% of calculated 1996 sales) 3 Commercial 3 6  73.4 116.4 4.47%

6 11 178.3 352.4 4.25%

11 16 1909.2 123.6 2.60%

16 25 340.4 392.8 4.19%

25 40 3405.2 164.0 2.28%

40 50 1724.5 265.0 3.39%

50 75 4933.4 240.5 2.30%

75 100 3297.8 346.1 3.07%

100 175 6662.2 116.6 1.25%

175 300 3172.6 177.3 2.45%

300 600 3377.5 12.8 0.35%

600 750 644.4 4.5 0.60%

1 See Equation 1. Time t is entered into the linear equation as ( t  1973), i. e., " 1973" is treated as t= 0, unlike the agricultural linear fit. 2 Defined as lower bound

rated power < upper bound. 3 Calculated in relative terms as the constant linear rate relative to 1996 year sales , i. e., glinPct ( See Equation 4). dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 14 Table 9 Fit Parameters and Annual Growth Rates for Linear Equipment Sales Trends for Industrial Engines1 Segment Size Class ( rated hp) 2 intercept ( c ) slope ( m) Linear Sales Growth Rate (% of calculated 1996 sales)

Industrial 3 6  104.9 22.1 5.48%

6 11  450.4 75.0 5.88%

11 16 372.5 172.1 3.97%

16 25  859.5 383.9 4.82%

25 40 2416.4 75.6 1.82%

40 50 273.3 351.1 4.21%

50 75 12763.1 462.8 1.98%

75 100 4766.8 90.8 1.32%

100 175 2135.5 156.0 2.73%

175 300 63.5 115.4 4.25%

300 600  64.5 15.3 5.32%

600 750  1.6 0.40 5.21%

1 See Equation 1. Time t is entered into the linear equation as ( t  1973), i. e., " 1973" is treated as t= 0, unlike the agricultural linear fit. 2 Defined as lower bound

rated power < upper bound. 3 Calculated in relative terms as the constant linear rate relative to 1996 year sales , i. e., glinPct ( See Equation 4). dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 15 Table 10 Fit Parameters and Annual Growth Rates for Logistic Equipment Sales Trends for Construction Equipment 1 Size Class ( rated hp) Upper bound ( K) Linear coefficient ( A) Growth Rate (  ) Lower Bound ( L) Ratio of Sales in Year to Sales in 2000 ( ry) 2 2001 2010 2020 2030 2040 2050 75 100 98,000 21.5 0.104 0.0 1.010 1.403 1.675 1.798 1.847 1.864 100 175 33,400 12,000 0.38 18,200 1.059 1.118 1.120 1.120 1.120 1.120 1 See Equation 3. Time t is entered into the logistic equation as ( t  1970), i. e., " 1970" is treated as t= 0.

2 See Equation 5. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 16 4.0 Population and Hp Hour Growth Projection Methods The equipment sales growth projections described above were used to generate equipment population projections, which were then combined with activity, hp, and load factors to generate total activity ( hp hour) growth rates for comparison to NONROAD's fuel consumption growth rates. Two versions of this basic approach were used. In Method # 1, the sales growth rates by size class were translated directly into corresponding population growth rates by size class. This method is described in Section 4.1 below. Method # 2 included the revised sales and population growth by size class as in Method # 1, but also placed a maximum limit on the expected life of equipment in the calculation of the populations, as described in Section 4.2 below.

4.1 Method 1: Estimating Equipment Population Growth from Sales Growth by Size Class To convert sales growth into population growth, we input sales estimates for the period 1950  2045 to an attrition model that applied scrappage rates to engines sold in successive years, using the same scrappage curves, median lives, activities and load factors used in NONROAD. In the sales history, the period 1973 2000 is represented by reported sales ( except as noted), the period 2001 to 2050 is represented by projected future sales, and the period 1950 to 1972 is represented by constant sales equal to the 1973 values. After applying appropriate scrappage rates to all sales cohorts ( equipment sold in a given model year) present in each year, the sum of remaining ( not yet scrapped) engines in each evaluation year constitutes the equipment population for that year ( N y). These calculations were performed individually for each hp size class within each equipment type to estimate equipment populations in each year from 1996 to 2045.

For purposes of comparison to population growth in the existing NONROAD model, these detailed population projections by equipment type or hp size class were then summed by industry segment for each of the four segments considered in this analysis. For each segment, a constant annual average growth rate for the period 1996 2030 was calculated ( g Pct9630), using the 1996 population as a fixed reference, similar to that calculated for linear sales projections.

This approach follows that used in NONROAD to project populations, allowing direct comparisons to projections from draft NONROAD2004, as well as among the different segments. To allow similar comparisons of growth trends over time, projected populations were also scaled to a relative basis as used in the NONROAD growth inputs. To put all projections on the same scale, each segment was assigned a ` reference population' i y of 1,000 in 1996. For years following 1996, i y is projected as the product of i 1996 (= 1,000) and the ratio of projected populations in 1996 and a given future year y, N 1996 and N y, respectively. In equation form, this calculation is g N N N Pct9630 =

     1 2030 1996 1996 2030 1996 ( 7)

i i N N y y =

     1996 1996 ( 8) dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 17 For example, if the projected diesel construction population in 2015 ( N 2015) were 2 million, and the corresponding 1996 population ( N 1996) were 1 million, then the ` reference population' would be 1,000 for 1996 ( i 1996) and 2,000 for 2015 ( i 2015). Use of the reference populations is another way of making direct comparisons to NONROAD's growth rates across multiple equipment segments.

4.2 Method 2: Estimating Equipment Population Growth with Bounded Median Lives This method is essentially the same as Method 1, except that the sales/ scrappage/ population calculation modified the median life values, capping them at an equivalent of 16 years. The NONROAD model assigns the age distributions of equipment in a given simulation year using the " annualized median life." We calculate annualized life ( l y, years) from median life at full load ( l h, hours) as where A is the annual activity ( hours/ year) and L is the load factor, which represents the average fraction of the engine's rated power used during operation. Thus, in addition to engine life, annualized life is dependent on assumptions regarding how much and how hard equipment is used. Examination of annualized life in relation to activity and load, shows that certain combinations of median life ( l h), activity and load factor give values of annualized median life that appear implausible. For example, as shown in Table 11, low values of load factor or activity, especially in combination, tend to result in long annualized lives, as in the case of welders, pressure washers or combines. On the other hand, high values of activity or load factor can give annualized lives that appear implausibly short as in the case of forklifts. Increasing the annualized life increases the persistence of engines in the population, slowing fleet turnover, and resulting in rapid population growth.

To address the possibility that long annualized median lives for some equipment types and size classes could be contributing to high population growth rates in NONROAD, we manipulated median life in the spreadsheet attrition model. Specifically, we constrained annualized median life to a maximum of 16 years for any equipment type or size class. Thus, because the scrappage curve is always symmetric about its median, the longest any engine could survive would be 32 years. These modifications to annualized median life were applied by equipment type and size class, as discussed above in Method 1. l l AL y h = ( 9) dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 18 Table 11 Annualized Median Lives for Selected Diesel Equipment Types Application Load factor ( L) Activity ( A, hr/ yr) Annualized Life ( ly, years)

<= 50 hp ( lh = 2,500 hr) 50 300 hp ( lh= 4,667 hr) 300 + hp ( lh= 7,000 hr)

Other Materials Handling 0.21 421 28.3 52.8 79.2 Welders 0.21 643 18.5 34.6 51.8 Skid steer loaders 0.21 818 14.6 27.2 Backhoes 0.21 1,135 10.5 19.6 Pressure washers 0.43 145 40.1 74.9 112.3 Generators 0.43 338 17.2 32.1 48.2 Refrigeration/ AC Units 0.43 1,341 4.3 8.1 Combines 0.59 150 52.7 79.1 Agricultural tractors 0.59 475 8.9 16.7 25.0 Crawler tractors 0.59 936 4.5 8.5 12.7 Off Highway trucks 0.59 1,641 4.8 7.2 Forklifts 0.59 1,700 2.5 4.7 7.0 5.0 Population and Hp Hour Growth Projection Results Table 12 and Figures 2 5 present comparisons of the diesel equipment growth projections from the NONROAD model to the alternative methods described above. The table presents annual average percentage growth rates with respect to the 1996 base year population ( g Pct9630, Equation 7), as well as an analogous growth rate for work output ( total hp hr). Work output is estimated as the product of total population ( N y), activity rates ( hr/ yr), average rated power ( hp) and load factor, and provides a more direct surrogate for comparison to fuel consumption as projected by the NONROAD model. The figures present the growth index i y ( Equation 8) for all methods, as a direct comparison of trends through 2045.

Based on our updated assessment of historic equipment sales, populations of some important equipment types are projected to grow at a fairly high rate over the next 30 years, e. g., 3 5%/ year. In contrast, the EIA Annual Energy Outlook ( AEO) projects fuel use as growing more slowly, e. g., < 1.5%/ year. Assumptions and approaches used in the analysis that account for the results obtained are discussed below. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 19 At the outset, projected sales growth is quite rapid for some size classes or equipment/ size combinations in the different segments. Strong sales growth would in itself result in rapid population growth, other factors being equal.

In addition, some important equipment types having high sales growth and large populations also have long annualized median lives. These factors in combination would also tend towards rapid population growth, as the sales " inputs" are large, and the scrappage " outgo" is relatively small. Rapid growth combined with a large population and moderate to high activity or load factor would imply substantial contributions to projected fuel consumption. At the same time, activity is assumed constant over the entire expected life for all equipment types, which in combination with long annualized lives would again increase contributions to work output ( hp hrs) and fuel consumption.

Finally, there is an important interaction in the sales/ scrappage model between annualized life and the base year population that influences projected future population growth rates. A central feature of the development of base year populations and growth rates for use in NONROAD is that the same annualized life estimates are used to project both base year and future populations. Thus, if a given set of sales projections is accepted as given, and if an estimated population growth rate appears high relative to an external counterpart, such as EIA's fuel consumption projections, one possibility is that scrappage rates are low, i. e., annualized life may be too long. However, if annualized lives are reduced by adjusting activity estimates or load factors, or by imposing maximum limits ( as in Method 2), resulting decreases in growth rates are smaller than might be expected, given the size of the reductions in annualized life.

To account for this counterintuitive result, it is important to remember that reducing annualized life accelerates scrappage in the " past" ( prior to the base year) as well as in the " future," ( following the base year). As a result, the base year population also decreases, which has a compensating effect on the population growth rate because the rate is calculated with respect to N 1996 as a fixed reference point ( see Equation 7), and a reduction in N 1996 has the effect of increasing the growth rate. This effect can be seen in a comparison of Methods 1 and 2 in Table 12. Population growth rates are consistently lower for Method 2 relative to Method 1, but the margins are small. Base year populations in each segment are also lower for Method 2. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 20 Table 12 Comparison of Growth Rates used in Draft NONROAD2004 and Alternative Sales Based Approaches ( This Analysis)

Method Backcast3 ( N1996) Population Growth ( gPct9630) 5 Hp Hour Growth4 ( gPct9630) 5 Agricultural Method 11 1,864,716 2.6% 4.4%

Method 22 1,507,722 2.7% 3.9%

Base = NONROAD2004 1,724,156 2.9% 2.9% Construction Method 1 1,362,150 7.0% 4.7%

Method 2 1,290,283 5.9% 4.3%

Base = NONROAD2004 1,463,829 3.2% 3.2% Commercial6 Method 1 668,484 5.4% 4.1%

Method 2 561,597 4.8% 3.7%

Base = NONROAD2004 773,213 4.5% 4.5% Industrial Method 1 353,031 4.8% 3.5%

Method 2 344,221 3.9% 3.4%

Base = NONROAD2004 394,240 3.5% 3.5%

1 " Method 1" = May 2003 sales data, sales projected linear & logistic by hp size class. 2 " Method 2" = Method 1, but with annualized median life capped at 16 years. 3 " Backcast" 1996 populations show that methods differ in past as well as future projections. 4 Hp Hour growth is used here as a surrogate to approximate growth in fuel consumption. 5 Average annual growth rates ( gPct9630) are given for year 2030 relative to 1996 ( Equation 7).

6Hydro Power Units are reclassified into the Commercial category for this analysis. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 21 Agricultural Equipment Growth 500 1000 1500 2000 2500 3000 3500 4000 1990 2000 2010 2020 2030 2040 2050 Year Relative Growth Index Method 1 Population Method 1 Pop* Hp Method 2 Population Method 2 Pop* Hp NONROAD Model Figure 2. Projected relative growth trends for agricultural equipment populations and total work output ( hp hr). The growth index iy ( Equation 9) is used to allow comparisons between Method 1, Method 2 and Draft NONROAD2004.

Construction Equipment Growth 500 1000 1500 2000 2500 3000 3500 4000 1990 2000 2010 2020 2030 2040 2050 Year Relative Growth Index Method 1 Population Method 1 Pop* Hp Method 2 Population Method 2 Pop* Hp NONROAD Model Figure 3. Projected relative growth trends for construction equipment populations and total work output ( hp hr). The growth index iy ( Equation 9) is used to allow comparisons between Method 1, Method 2 and Draft NONROAD2004. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 22 Industrial Equipment Growth 500 1000 1500 2000 2500 3000 3500 4000 1990 2000 2010 2020 2030 2040 2050 Year Relative Growth Index Method 1 Population Method 1 Pop* Hp Method 2 Population Method 2 Pop* Hp NONROAD Model Figure 5. Projected relative growth trends for industrial equipment populations and total work output ( hp hr). The growth index iy ( Equation 9) is used to allow comparisons between Method 1, Method 2 and Draft NONROAD2004. Commercial Equipment Growth 500 1000 1500 2000 2500 3000 3500 4000 1990 2000 2010 2020 2030 2040 2050 Year Relative Growth Index Method 1 Population Method 1 Pop* Hp Method 2 Population Method 2 Pop* Hp NONROAD Model Figure 4. Projected relative growth trends for commercial equipment populations and total work output ( hp hr). The growth index iy ( Equation 9) is used to allow comparisons between Method 1, Method 2 and Draft NONROAD2004. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 23 6. Conclusions In the course of these analyses, we developed revised growth rates for nonroad equipment populations. The revised population growth rates were derived in turn from sales growth rates developed at higher levels of detail than those currently used in the NONROAD model, i. e., rates were developed to represent individual equipment types or size classes, or combinations of equipment type and size. While the revised rates may better represent possible trends in populations of smaller equipment, they do not reduce projected growth in fuel consumption in nonroad equipment to levels comparable to projections published by the Energy Information Administration. Due to the difficulties in achieving such reconciliation, we have not modified the growth rates used in Draft NONROAD2004 for purposes of the final rulemaking for diesel engines. However, as mentioned, we have performed a sensitivity analysis in which NONROAD's fuel consumption and emissions estimates were adjusted to match fuel consumption projections for nonroad equipment derived from EIA's Annual Energy Outlook 2003 ( Chapter 8, Appendix A. 2). This sensitivity case is designed to assess the appropriateness of the new emission standards under an assumption that EIA's projections were correct. Additionally, we will continue to evaluate the estimation of growth in nonroad equipment populations in the future. As EPA incorporates nonroad emissions into the MOVES model, we hope to resolve these issues to improve our confidence in current and future projections of nonroad emissions. dwcgi 6994 1082755303 324186000. wpd DRAFT Mar 11, 2004 24 APPENDIX A Memorandum describing Development of Sales Projections for Agricultural Nonroad Equipment performed by Research Triangle Institute International TO: Todd Sherwood, EPA Office of Transportation Air Quality FROM: Andrea Thomas Bill White DATE: August 25, 2003 SUBJECT: Horsepower Based Growth Rates for Agricultural Engines  Final Report This memo presents projected growth rates for sales of diesel powered agricultural equipment for the period 2001 2050. In completing this work, we have used a variety of standard statistical techniques to analyze the 30 years of sales data provided by EPA and to project these historical sales trends into the future. This effort is consistent in technique and results with recent estimates generated by EPA's Office of Transportation Air Quality. An alternative approach, involving creation of a new model that would link agricultural equipment purchases to projections of changes in underlying technological and economic aggregates, was not pursued, because of concerns about complexity and cost.

This memorandum is divided into three major sections. The first section contains an analysis of the engine sales database EPA provided to RTI to use in this analysis. In the second section, we discuss the statistical techniques that we used to generate sales projections based on the historical data and the reasons for selecting the chosen techniques. In the final section, we present the resulting growth estimates for each application area and engine size class. To make this report more useful, we have included two appendices containing tables of the year by year sales projections for each application/ size class and a third appendix with thumbnail graphs.

I. Summary and Analysis of Diesel Powered Agricultural Equipment Database As the source material for new long run growth estimates, the database EPA provided us includes approximately 30 years of sales data for nonroad agricultural engines, with detailed information on engine size and application area. This database, known as PSR OE LinkTM, is compiled and provided to EPA by PSR ( Power Systems Research, 2003). We used this data source extensively for our analysis, in which we looked at trends in reported sales, separated into application areas and horsepower classes as used by EPA in the EIA for the proposed rule.

Broad Trends in Equipment Aggregates. The most striking feature of the important tractor and combine segments, as well as for several of the smaller application groups, is the huge increase in Todd Sherwood August 25, 2003 Page 2 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000 220,000 240,000 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Annual Units Sold PSR Data Census: Apparent Consumption Figure 1. Sales Agriculture Tractor   1970 2000 sales during the 1970s and subsequent crash during the early 1980s. Figure 1 shows these impacts quite clearly for the agricultural tractor application area. These unusual trends resulted primarily from the agricultural boom fueled by the 1970s grain sales to the Soviet Union, which attracted many new entrants into farm operations. These newcomers needed to purchase or lease new equipment to get into the business; many also borrowed money to lease farmland as well. After the Soviet invasion of Afghanistan in 1979, President Carter placed an embargo on grain sales to Russia, and markets for grain, farmland, and farm equipment all crashed. It took several years for the equipment glut to clear through the used equipment market and for sales of new machines to recover to equilibrium levels.

The second significant overall trend is one of strong growth from the mid 1990s through the present, which Figure 1 also shows. Combines, sprayers, and irrigation equipment also show this recent increase in sales, although there is considerable variation between horsepower class segments. A ready explanation is not available for this recent time period, unlike the 1975 to 1985 period. In fact, farming has reportedly been in almost continual crisis since passage of the 1996 Freedom to Farm Act, which attempted to reform the nation's price support system. It is Todd Sherwood August 25, 2003 Page 3 quite possible that these increases may have resulted from increased liquidity among farm operators following large " emergency payments" voted by Congress each year from 1997 onward. Although a definitive explanation is beyond the scope of this task, the potential of liquidity induced purchases suggests that care should be exercised in extrapolating recent data out many years into the future.

Figure 1 also provides some indication of the comparability of the PSR database for farm tractors to another source of historical shipments data, the Census Bureau's annual industrial reports on farm tractor production and consumption ( U. S. Department of Commerce, 1985; U. S. Department of Commerce, 1990). In these reports, domestic production, exports, and imports for consumption are listed separately, allowing a calculation of " apparent consumption" for each year. Unfortunately, the Census Bureau changed its method of calculating the totals several times during the 1970s and 1980s and discontinued the report completely after 1989. Nonetheless, the two sources track fairly well during the period of overlap, except for the first 4years of PSR data; this issue is discussed further in the second section of this memorandum.

Trends in Sales by Size and Application Areas. When the database information is broken down by horsepower class within each application area, other concerns emerge. It appears that detailed model specific data are only available in the database from 1990 to 2000. Before that time, there is only one record for each year in each size class, which suggests that units of varying size were lumped together in the record. The resulting " heaping" of data around the reported horsepower values makes statistically valid inferences more difficult. More importantly, however, the reported horsepower values change at intervals during the 30 year time period, often jumping from one size segment to another. Figure 2 shows this phenomena for windrowers; note that the 50 to 75 hp category drops to zero in the reported data after 1985, at which point the adjacent 75 to 100 hp category immediately increases. The large engine 100 to 175 hp class, which is close to zero for most of the period, jumps to more than 500 units per year once model level data became available in 1990. This is accompanied by corresponding drop in the 75 to 100 hp range.

These heaping and size class instability phenomena make independent estimation of growth rates by horsepower size class difficult. In addition, the small sample sizes noted in the 1998 EPA memorandum that summarized a previous version of these growth projections are unchanged as an estimation issue ( Dolce, 1998). We have found, however, that some reaggregation of size classes can yield consistent time series, with decent sample sizes, at least for a few of the application areas. For example, combining all of the windrower size classes removes the anomalous jumps noted earlier, revealing a much smoother underlying trend. Todd Sherwood August 25, 2003 Page 4 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Annual Units Sold 50 75hp 75 100hp 100 175hp Figure 2. Windrower Sales by HP Class 1973 2000 Suitability for Development of Requested Growth Estimates. With the qualification noted above, we concluded that the PSR database provided will be more than adequate to provide a basis for creating growth estimates for the 2001 2050 time horizon. Truncation of some of the early years' data will help avoid distortions caused by the Soviet grain bubble and mitigate any effects of the PSR's low sales figures in the 1973 1976 period. Selective combining of adjacent size class series will eliminate heaping and data instability issues. The results of these modifications will be discussed in more detail in the next section of this memorandum.

Other Potential Databases. We are not aware of any comprehensive sources of agricultural equipment data that report unit sales by model, as the PSR does. The Agricultural Equipment Manufacturers ( AEM) trade group reports monthly tractor sales by major type and size group, including two wheel drive ( 2WD) units under 100 hp, 2WD over 100 hp, and 4WD. The AEM also reports on combines. Unfortunately, AEM does not release historical data, because their Todd Sherwood August 25, 2003 Page 5 focus is primarily to highlight current market conditions. We did not believe that it would be advisable to contact this group directly to ask if they maintained and would be willing to share long run data.

Until recently, the Census Bureau's annual reports series noted above, along with their predecessor, Current Industrial Reports ( CIR), would have been quite useful in providing aggregate sales of tractors and other major pieces of farm equipment. The Economic Census reports, published every 5 years, have also been helpful in the past to supplement the annual CIR data. With the long term decline and recent consolidation of the agricultural equipment industry, there are so few producing firms remaining that the Census Bureau has stopped publishing unit volume information. Likewise, the annual Agricultural Statistics published by the U. S. Department of Agriculture also no longer contains unit production and sales figures for tractors, although they report on several classes of minor equipment.

II. Approach for Generating Growth Estimates EPA has used a variety of methods to estimate near term movements in sales and equipment populations, most notably projections of economic indicators and historic trend analysis. The Bureau of Economic Analysis's ( BEA's) periodic regional projections proved especially useful in supporting the former in that they provided a 50 year look ahead at growth rates by industrial sector. If the researcher were willing to assume that each equipment category's share of total output would remain unchanged over half a century, the aggregate economic trends could be converted into equipment specific ones. Unfortunately for our purposes, the BEA eliminated their regional projections program in 1996, and their last report, containing 1995 to 2045 projections, is now quite out of date.

In the absence of the BEA economic forecasts, it might still be possible to use long term estimates of national GDP or population to generate projections for sales of diesel powered agricultural equipment. By understanding the way this equipment is used and uncovering the technical relationships between inputs, outputs, and economic or demographic drivers, it would be possible to bring appropriate economic variables to bear on the estimation task. In fact, the Census Bureau published 100 year population projections for the U. S. after every decennial census and makes them available on its website ( U. S. Department of Commerce, 2000).

Embarking on a new modeling effort, involving the complex relationships between population, food consumption, imports and exports, crop yields, and equipment inputs, would be quite costly and complex. The assumptions needed to inform such a model carry sufficient uncertainty that it is possible that such estimates would be more speculative that extrapolation of recent trends. For Todd Sherwood August 25, 2003 Page 6 this reason, RTI and EPA elected to adopt the simpler approach recommended by the previous Dolce report.

Overall Approach By electing to use statistical methods to analyze the sales data and create projections, our task became that of fitting the data to a smooth curve and extrapolating that curve over the 2001 to 2050 time period. Still, decisions were necessary about a number of data and analytical issues, including decisions on combining size classes into aggregate series; potential truncation in the early years; and, most importantly, the type of equation to use in fitting the trends. As EPA had noted in previous communication with RTI, the raw data were not amenable to simple leastsquares regression, either to fit linear trends or constant growth rate ( exponential) projections. Although we did not modify any of the data presented to us, we truncated or combined several series in an attempt to make the data series more consistent and to eliminate distortions created by macroeconomic shocks and defects in the PSR data. We describe each of these steps in the text that follows.

Combining Data Series We described in the first section of this memorandum one of several instances in which there were discontinuities in adjacent PSR data series, appearing to result from changes in definition or perhaps re testing or re engineering of popular equipment models. By combining these series, and therefore using a common growth rate for both size classifications, more consistent and precise trends could be estimated. Of the 35 equipment/ engine size categories for which RTI was given PSR data ( excluding hydraulic power units, which were later excluded), we created 24 separate 50 year estimates.

Truncating Data Series Where we had information that inclusion of data from the early years of the 1970 2000 time period would likely distort the curve fitting, we opted to truncate the data series. This was done for two reasons: distortions from the late 1970s boom/ early 1980s bust in grain sales, which affected tractors, combines, and a couple of other equipment types, and apparent incomplete records for several equipment categories in the first few years of the PSR database. As we discussed in the Task 8 memorandum, for instance, tractor totals do not match census values until the late 1970s, and many other series have very low numbers in the mid 1970s. A total of five data series were truncated prior to 1982 to eliminate the boom/ bust distortion; in three series, we eliminated extremely low sales estimates from the 1973 1976 period. Todd Sherwood August 25, 2003 Page 7 Selection of Equation for Fitting the Data In previous projections of future equipment sales, EPA has most often estimated a constant growth rate over time, which is equivalent to fitting the data to an exponential growth curve. The following equation describes this type of fit:

y = exp © + mT) ( 1)

where y is the unit sales for year T, and c and m are intercept and slope parameters, respectively. The constant growth rate function has the advantage of simplicity of estimation and use. For those data series where sales are declining over the period in question, it has a second advantage of preventing the estimation of negative sales values out in the distant future. Nonetheless, for long range extrapolation, exponential growth fits tend to produce unreasonably large projected sales in the out years, unless the underlying growth rate is very small. In the present study, all of the declining series and two of the ( very slowly) growing series were estimated using the constant growth rate equation.

For a data series that is increasing through time, a linear fit can avoid the tendency of the estimates to explode in the long run. Such a fit can be described by Eq. ( 2):

y = c + mT ( 2)

where each of the variables has the same meaning as in Eq. ( 1). For a series growing in a linear fashion, the steady increase in the number of units compared with an increasing base means that the growth rate falls slowly over time. The projected behavior is consistent with much of the last 50 to 60 years of agricultural experience. We judged that three of the agricultural equipment data series were best fitted to a linear equation.

Unfortunately, neither exponential nor linear models are very useful for estimating the long run behavior of products that are experiencing rapid growth during the base period. The new products literature in economics and business management, along with studies of biological growth processes, have increasingly stressed the use of equations that trace out the S shaped growth curve most often experienced. Several of these equations, including the logistic, Gompertz, and Richards functions, can be described with three or four parameters, making them suitable for the present task, where we have a limited number of observations. The logistic function can be modeled using the following equation:

y = { K / [ 1 + A * ( exp ( B * T)]} + L ( 3) Todd Sherwood August 25, 2003 Page 8 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 K  Asymptote = 65,000 B  Maximum Slope A  Onset Figure 3. Illustration of Logistic Curve with y and T as before, and K, A, B, and L empirically derived parameters. In the simplest logistic formulations, L is excluded; its presence allows the base of the S shaped curve to be lifted above zero.

Each of the other parameters has an interpretation in the logistic model, as illustrated in Figure 3. The value of K determines the maximum value the logistic function will attain ( i. e., the asymptote of the function). ( If L is greater than zero, the asymptote is actually L + K.) The B parameter affects the slope of the function at its inflection point, the time at which the slope is the greatest. The value of A determines the onset of growth or the position of the S curve along the time axis.

Empirically, the values of the four parameters are estimated by minimizing the mean square error of the fit, a process that can be done by any of a number of readily available equation solvers ( Cavallini, 1993). RTI judged that four rapidly growing series, including the two smallest agricultural tractor classes, were best described by logistic fits. Upon EPA's request, RTI also Todd Sherwood August 25, 2003 Page 9 elected to provide linear fits for these data series ( which are included in the alternate projections in Appendix B and illustrated in the thumbnail graphs in Appendix C).

III. Growth Rate and Sales Projections The growth rate estimates appear in Tables 1, 2, and 3. For those series fitted to an exponential function, the constant growth rates are included in Table 1. Representative growth rates for each decade over the next 50 years are shown for the series fitted to a linear model in Table 2 and for logistic models in Table 3. It should be noted that sales projections for a number of these declining series approach zero over the 50 year time horizon. At the other extreme, the use of linear or logistic curves to describe the rapidly growing application/ size classes prevented 2050 estimates for these categories from exceeding the total population of agricultural equipment in place or the number of farms being operated today. The four series we fitted to logistic curves show a close correspondence to the existing data and yield growth projections that we find to be very reasonable. Figure 4 illustrates a comparison between the projections of the three types of curve fits and shows the hazard of using constant growth rates to project future sales for series that show rapid growth over the recent horizon.

In the appendices to this memorandum, we have included tables containing sales projections by application area, by engine class, for each year from 2001 to 2050, along with the PSR totals for each category and the parameters estimated statistically for each model fit. RTI's recommended projections, including constant growth rate ( exponential), linear, and logistic fits, appear as the base case in Appendix A. An alternative set of projections, substituting linear fits for those initially fitted to logistic functions, is included ] as Appendix B. Finally, Appendix C contains thumbnail graphs of all 24 series, with both base and alternate fit lines included for comparison. Todd Sherwood August 25, 2003 Page 10 Table 1. Annual Growth Rates for Agricultural Equipment Data Series Application Engine Size Base Period Type of Fit Annual Growth Rate Agricultural Tractors 11 25 hp 1980 2000 Logistic See Table 3 Agricultural Tractors 26 50 hp 1973 2000 Logistic See Table 3 Agricultural Tractors 51 100 hp 1973 2000 Exponential  1.68 %

Agricultural Tractors 101 175 hp 1982 2000 Linear See Table 2 Agricultural Tractors 176 hp and up 1982 2000 Linear See Table 2 Combines 76 100 hp 1973 2000 None Nil Combines 101 175 hp 1982 2000 Exponential  14.5 %

Combines 175 hp and up 1982 2000 Linear See Table 2 Windrowers all hp 1977 2000 Exponential  2.21 %

Balers all hp 1982 2000 Exponential + 0.59 %

Sprayers 11 100 hp 1977 2000 Exponential  0.85 %

Sprayers 101 hp and up 1973 2000 Logistic See Table 3 Irrigation Sets 11 25 hp 1973 2000 Exponential  5.56 %

Irrigation Sets 26 50 hp 1973 2000 Exponential  11.32 %

Irrigation Sets 51 75 hp 1973 2000 Exponential  11.33 %

Irrigation Sets 76 100 hp 1973 2000 Exponential + 0.01 %

Irrigation Sets 101 175 hp 1973 2000 Exponential  1.69 %

Irrigation Sets 175 hp and up 1973 2000 Exponential  0.22 %

Other Ag Equipment 11 50 hp 1973 2000 Exponential  2.12 %

Other Ag Equipment 51 75 hp 1973 2000 Exponential  13.24 %

Other Ag Equipment 76 100 hp 1977 2000 Exponential  1.46 %

Other Ag Equipment 101 175 hp 1989 2000 Exponential  2.90 %

Other Ag Equipment 176 300 hp 1989 2000 Exponential  0.35 % Todd Sherwood August 25, 2003 Page 11 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 Annual Units Sold PSR Data Logistic Linear Exponential Figure 4. Projected Sales of 26 50hp Ag Tractors Figure 4. Projected Sales of 26 50hp Ag Tractors Other Ag Equipment 301 hp and up 1989 2000 Logistic See Table 3 Table 2. Annual Growth Rates by Decade for Linear Series Projections Application Engine Size 2001 10 2011 20 2021 30 2031 40 2041 50 Agricultural Tractors 101 175 hp + 2.76 % + 2.16 % + 1.78 % + 1.51 % + 1.31 %

Agricultural Tractors 176 hp and up + 2.60 % + 2.06 % + 1.71 % + 1.46 % + 1.27 %

Combines 175 hp and up + 1.33 % + 1.17 % + 1.05 % + 0.95 % + 0.85 %

Table 3. Annual Growth Rates by Decade for Logistic Series Projections Application Engine Size 2001 10 2011 20 2021 30 2031 40 2041 50 Agricultural Tractors 11 25 hp + 8.01 % + 4.22 % + 1.56 % + 0.46 % + 0.13 %

Agricultural Tractors 26 50 hp + 2.37 % + 1.17 % + 0.52 % + 0.22 % + 0.09 %

Sprayers 101 hp and up + 0.89 % + 0.03 % nil nil nil Todd Sherwood August 25, 2003 Page 12 Other Ag Equipment 301 hp and up + 0.31 % nil nil nil nil References Cavallini, Fabio. 1993. " Fitting a Logistic Curve to Data." College Mathematics Journal 24( 3): 247 53.

  Dolce, Gary J. 1998. " Nonroad Engine Growth Estimates." U. S. Environmental Protection Agency, Office of Mobile Sources, Report No. NR 008. Washington, DC: EPA.

Power Systems Research ( PSR). 2003. OELink   . < http:// www. powersys. com/ OELink. htm>.

U. S. Department of Commerce, Bureau of the Census. 1985. " Tractors ( Except Garden Tractors)." M35S( 83) 13, and previous annual summaries. Washington, DC: U. S. Department of Commerce.

U. S. Department of Commerce, Bureau of the Census. 1990. " Farm Machinery and Lawn and Garden Equipment." MA35A( 89) 1, and previous annual summaries. Washington, DC: U. S. Department of Commerce.

U. S. Department of Commerce, Bureau of the Census. 2000. " Annual Projections of the Total Resident Population as of July 1: Middle, Lowest, Highest, and Zero International Migration Series, 1999 2100." Washington: Population Projections Program, Population Division. Todd Sherwood August 25, 2003 Page 13 Appendix A. Tables of Sales Projections Data   Base Case Ag Tractor Combines Windro wers Balers Sprayers Irrigation Sets Other Ag Ag Mowers Year 10< x# 2 5 25< x# 5 0 50< x# 7 5 75< x#

100 100< x# 1 75 > 175 75< x# 1 00 100< x#

175 > 175 All All 10< x# 1 00 > 100 10< x# 25 25< x# 50 50< x# 75 75< x# 10 0 100< x# 1 75 > 175 10< x# 5 0 50< x# 7 5 75< x# 1 00 100< x#

175 175< x#

300 > 300 All Historical 1973 0 13,805 11,388 9,698 30,545 8,097 595 3,584 2,633 718 15 42 118 14 302 182 744 779 209 230 86 17 Data 1974 0 13,849 13,703 12,159 35,372 9,725 785 3,568 3,100 983 44 44 137 27 571 160 806 870 376 220 80 35 ( from PSR) 1975 0 9,881 13,442 16,465 43,681 13,399 1,178 4,025 4,557 697 111 62 69 37 668 264 1,078 1,123 578 147 119 49 1976 0 9,713 11,199 18,007 50,847 17,175 1,587 6,307 6,077 208 126 71 65 73 1,316 300 1,229 1,567 746 197 147 51 1977 0 8,816 9,993 20,038 60,145 28,152 1,133 13,456 8,288 736 194 119 80 211 979 468 1,733 2,307 687 292 178 143 172 1978 0 7,715 9,551 18,061 53,740 26,933 778 14,017 8,201 3,447 313 144 130 90 187 717 2,282 4,198 624 564 261 219 151 1979 2 11,643 12,175 20,207 57,395 33,776 1,446 14,557 8,456 2,828 308 197 160 158 480 802 2,912 5,176 797 653 268 290 173 1980 1,091 21,630 20,778 20,105 44,232 28,052 1,651 10,456 7,868 3,678 118 617 227 218 286 719 3,087 4,765 910 583 168 259 147 11 1981 1,088 22,852 18,323 19,367 29,834 22,544 1,510 9,630 7,712 3,924 264 609 130 182 280 685 2,468 3,673 555 450 564 226 122 15 1982 1,087 25,484 17,201 17,903 24,551 20,394 964 6,509 6,653 2,007 263 555 122 127 294 512 1,851 2,756 427 381 634 245 125 17 1983 1,089 26,396 16,231 15,833 18,682 17,396 808 5,748 6,026 1,887 296 612 130 169 262 506 1,745 2,499 357 304 604 242 129 16 1984 1,883 25,788 15,470 14,478 16,075 16,246 1,263 6,471 5,830 2,082 142 344 139 140 358 429 1,427 2,196 315 277 257 144 103 3 60 1985 2,045 24,169 14,535 12,656 8,346 11,538 758 4,152 3,515 3,304 147 353 154 145 374 352 1,411 2,170 380 293 272 128 74 4 64 1986 2,191 23,416 13,854 11,369 7,237 6,302 0 4,991 2,339 3,297 189 367 128 141 388 154 1,467 1,864 362 251 115 97 56 0 65 1987 3,617 21,686 11,963 10,657 8,727 8,107 16 5,999 5,266 4,102 180 336 115 95 228 112 1,350 1,622 418 254 119 94 55 0 58 1988 3,843 20,171 11,534 10,645 9,600 17,221 13 2,895 4,501 2,725 235 204 627 107 237 110 1,597 1,799 501 277 133 103 59 0 58 1989 2,605 20,714 11,534 10,565 9,013 23,217 15 2,637 7,669 2,614 212 232 658 118 259 121 1,377 1,687 471 178 4 334 489 504 70 50 1990 6,314 30,260 9,154 13,420 10,032 30,550 11 2,629 4,444 2,270 307 275 1,125 4 18 11 870 1,030 405 189 0 171 808 735 17 47 1991 6,951 37,759 9,951 12,346 7,738 21,022 16 2,228 4,253 2,128 234 271 1,617 5 43 39 1,221 1,272 422 200 0 138 740 796 15 45 1992 6,771 39,100 8,245 8,207 8,811 19,494 10 2,119 4,095 1,963 228 281 2,063 6 40 42 1,066 1,083 398 200 4 145 733 810 172 45 1993 7,761 39,983 6,534 5,504 11,091 19,763 9 681 5,316 2,147 229 278 2,336 25 56 42 1,284 1,155 437 211 4 156 686 739 191 45 1994 7,880 38,761 9,082 5,753 13,881 22,911 7 378 5,092 2,231 237 322 2,508 29 61 46 1,435 1,261 476 220 3 162 750 780 222 45 1995 8,135 34,458 10,803 6,290 14,657 24,525 3 346 5,542 2,302 225 281 2,580 33 68 54 1,572 1,385 501 233 3 171 828 754 248 45 1996 8,744 38,158 12,541 7,881 16,392 29,731 5 1,329 7,569 2,273 208 332 2,981 34 71 55 1,575 1,439 517 203 29 168 630 665 473 44 1997 8,129 42,609 13,723 9,344 28,487 31,362 3 809 7,933 2,280 230 324 4,040 35 73 55 1,577 1,502 532 188 43 161 518 780 463 45 1998 11,049 41,423 12,685 11,077 31,884 31,603 6 860 8,885 2,018 254 235 4,254 36 75 56 1,622 1,548 549 200 48 147 541 758 559 47 1999 14,086 45,959 15,687 11,401 26,980 28,846 5 873 5,780 1,918 211 194 4,012 180 50 143 493 643 540 2000 15,522 54,773 16,849 12,271 28,072 20,435 0 989 4,673 1,927 212 204 4,015 179 42 167 499 556 480 Projection s 2001 16,308 49,275 11,616 7,971 23,081 29,865 0 443 6,460 1,917 233 264 4,246 23 33 27 1,482 1,366 460 188 7 140 522 687 550 38 2002 18,023 50,774 11,565 7,744 23,809 30,743 0 383 6,551 1,875 234 261 4,298 22 29 24 1,482 1,343 459 184 6 138 507 685 555 37 2003 19,849 52,226 11,514 7,523 24,537 31,621 0 331 6,643 1,834 235 259 4,332 20 26 21 1,482 1,320 458 180 6 136 493 682 557 36 2004 21,779 53,627 11,463 7,309 25,265 32,499 0 286 6,734 1,794 237 257 4,355 19 23 19 1,483 1,298 457 176 5 134 479 680 558 35 2005 23,803 54,976 11,412 7,101 25,992 33,376 0 248 6,825 1,755 238 255 4,370 18 21 17 1,483 1,276 456 172 4 132 465 678 559 34 2006 25,911 56,269 11,362 6,898 26,720 34,254 0 214 6,917 1,717 239 253 4,380 17 19 15 1,483 1,255 455 169 4 130 452 675 560 33 2007 28,087 57,505 11,312 6,702 27,448 35,132 0 185 7,008 1,679 241 251 4,387 16 17 14 1,483 1,234 454 165 3 128 439 673 560 32 2008 30,313 58,683 11,262 6,511 28,176 36,010 0 160 7,100 1,642 242 248 4,392 15 15 12 1,484 1,213 453 162 3 126 426 671 560 31 2009 32,573 59,803 11,212 6,325 28,904 36,888 0 138 7,191 1,606 244 246 4,394 15 13 11 1,484 1,193 452 158 3 125 414 668 560 30 2010 34,844 60,864 11,163 6,145 29,631 37,765 0 119 7,282 1,571 245 244 4,396 14 12 10 1,484 1,173 451 155 2 123 402 666 560 30 2011 37,109 61,868 11,114 5,970 30,359 38,643 0 103 7,374 1,537 247 242 4,398 13 11 9 1,484 1,153 450 152 2 121 391 663 560 29 2012 39,346 62,814 11,065 5,800 31,087 39,521 0 89 7,465 1,503 248 240 4,398 12 9 8 1,484 1,134 449 148 2 119 379 661 560 28 2013 41,536 63,705 11,016 5,635 31,815 40,399 0 77 7,556 1,470 250 238 4,399 12 8 7 1,485 1,115 448 145 2 117 369 659 560 27 2014 43,663 64,542 10,967 5,474 32,543 41,276 0 67 7,648 1,438 251 236 4,399 11 8 6 1,485 1,096 447 142 1 116 358 657 560 27 2015 45,710 65,326 10,919 5,318 33,271 42,154 0 58 7,739 1,407 253 234 4,400 10 7 5 1,485 1,078 446 139 1 114 348 654 560 26 2016 47,665 66,059 10,871 5,167 33,998 43,032 0 50 7,830 1,376 254 232 4,400 10 6 5 1,485 1,060 445 136 1 112 338 652 560 25 2017 49,519 66,744 10,823 5,020 34,726 43,910 0 43 7,922 1,346 256 230 4,400 9 5 4 1,486 1,042 444 134 1 111 328 650 560 24 2018 51,262 67,382 10,775 4,877 35,454 44,788 0 37 8,013 1,316 257 228 4,400 9 5 4 1,486 1,024 443 131 1 109 319 647 560 24 2019 52,891 67,977 10,727 4,738 36,182 45,665 0 32 8,105 1,287 259 226 4,400 8 4 3 1,486 1,007 442 128 1 108 310 645 560 23 2020 54,404 68,529 10,680 4,603 36,910 46,543 0 28 8,196 1,259 260 224 4,400 8 4 3 1,486 990 441 125 1 106 301 643 560 22 2021 55,800 69,042 10,633 4,472 37,637 47,421 0 24 8,287 1,232 262 223 4,400 8 3 3 1,486 974 440 123 1 105 292 641 560 22 2022 57,082 69,517 10,586 4,344 38,365 48,299 0 21 8,379 1,205 263 221 4,400 7 3 2 1,487 957 439 120 0 103 284 638 560 21 2023 58,253 69,957 10,539 4,221 39,093 49,177 0 18 8,470 1,178 265 219 4,400 7 3 2 1,487 941 438 118 0 102 276 636 560 21 2024 59,317 70,364 10,493 4,100 39,821 50,054 0 16 8,561 1,153 266 217 4,400 6 2 2 1,487 925 437 115 0 100 268 634 560 20 ( continued) Todd Sherwood August 25, 2003 Page 14 Appendix A. Tables of Sales Projections Data   Base Case ( continued)

Ag Tractor Combines Windro wers Balers Sprayers Irrigation Sets Other Ag Ag Mowers Year 10< x# 2 5 25< x# 5 0 50< x# 7 5 75< x#

100 100< x# 1 75 > 175 75< x# 1 00 100< x#

175 > 175 All All 10< x# 1 00 > 100 10< x# 25 25< x# 50 50< x# 75 75< x# 10 0 100< x# 1 75 > 175 10< x# 5 0 50< x# 7 5 75< x# 1 00 100< x#

175 175< x#

300 > 300 All 2025 60,280 70,741 10,446 3,984 40,549 50,932 0 13 8,653 1,127 268 215 4,400 6 2 2 1,487 910 436 113 0 99 260 632 560 20 2026 61,149 71,088 10,400 3,870 41,276 51,810 0 12 8,744 1,103 269 213 4,400 6 2 2 1,488 895 435 110 0 97 253 629 560 19 2027 61,930 71,409 10,354 3,760 42,004 52,688 0 10 8,835 1,079 271 212 4,400 5 2 1 1,488 880 434 108 0 96 245 627 560 18 2028 62,629 71,704 10,309 3,653 42,732 53,565 0 9 8,927 1,055 273 210 4,400 5 2 1 1,488 865 433 106 0 94 238 625 560 18 2029 63,255 71,977 10,263 3,549 43,460 54,443 0 8 9,018 1,032 274 208 4,400 5 1 1 1,488 850 432 104 0 93 232 623 560 17 2030 63,812 72,227 10,218 3,448 44,188 55,321 0 6 9,110 1,009 276 206 4,400 5 1 1 1,488 836 431 101 0 92 225 621 560 17 2031 64,308 72,458 10,173 3,349 44,916 56,199 0 6 9,201 987 278 205 4,400 4 1 1 1,489 822 430 99 0 90 218 618 560 17 2032 64,748 72,670 10,128 3,254 45,643 57,077 0 5 9,292 966 279 203 4,400 4 1 1 1,489 808 429 97 0 89 212 616 560 16 2033 65,137 72,865 10,083 3,161 46,371 57,954 0 4 9,384 945 281 201 4,400 4 1 1 1,489 795 428 95 0 88 206 614 560 16 2034 65,482 73,044 10,039 3,071 47,099 58,832 0 4 9,475 924 282 199 4,400 4 1 1 1,489 781 427 93 0 86 200 612 560 15 2035 65,787 73,208 9,994 2,984 47,827 59,710 0 3 9,566 904 284 198 4,400 3 1 1 1,490 768 426 91 0 85 194 610 560 15 2036 66,056 73,359 9,950 2,899 48,555 60,588 0 3 9,658 884 286 196 4,400 3 1 1 1,490 755 425 89 0 84 189 608 560 14 2037 66,293 73,497 9,906 2,816 49,282 61,465 0 2 9,749 865 288 194 4,400 3 1 0 1,490 743 424 87 0 83 183 606 560 14 2038 66,502 73,624 9,863 2,736 50,010 62,343 0 2 9,840 846 289 193 4,400 3 0 0 1,490 730 423 86 0 82 178 603 560 14 2039 66,686 73,741 9,819 2,658 50,738 63,221 0 2 9,932 827 291 191 4,400 3 0 0 1,490 718 422 84 0 80 173 601 560 13 2040 66,848 73,847 9,776 2,582 51,466 64,099 0 2 10,023 809 293 190 4,400 3 0 0 1,491 706 421 82 0 79 168 599 560 13 2041 66,990 73,945 9,733 2,509 52,194 64,977 0 1 10,115 791 294 188 4,400 2 0 0 1,491 694 421 80 0 78 163 597 560 12 2042 67,115 74,035 9,690 2,437 52,922 65,854 0 1 10,206 774 296 186 4,400 2 0 0 1,491 682 420 79 0 77 159 595 560 12 2043 67,224 74,117 9,647 2,368 53,649 66,732 0 1 10,297 757 298 185 4,400 2 0 0 1,491 671 419 77 0 76 154 593 560 12 2044 67,320 74,192 9,604 2,300 54,377 67,610 0 1 10,389 740 300 183 4,400 2 0 0 1,492 660 418 75 0 75 150 591 560 11 2045 67,405 74,261 9,562 2,235 55,105 68,488 0 1 10,480 724 301 182 4,400 2 0 0 1,492 649 417 74 0 74 145 589 560 11 2046 67,479 74,324 9,520 2,171 55,833 69,365 0 1 10,571 708 303 180 4,400 2 0 0 1,492 638 416 72 0 73 141 587 560 11 2047 67,543 74,382 9,478 2,109 56,561 70,243 0 1 10,663 693 305 179 4,400 2 0 0 1,492 627 415 71 0 72 137 585 560 11 2048 67,600 74,434 9,436 2,049 57,288 71,121 0 0 10,754 678 307 177 4,400 2 0 0 1,492 617 414 69 0 70 133 583 560 10 2049 67,650 74,483 9,394 1,991 58,016 71,999 0 0 10,845 663 309 176 4,400 2 0 0 1,493 606 413 68 0 69 129 581 560 10 2050 67,694 74,527 9,353 1,934 58,744 72,877 0 0 10,937 648 310 174 4,400 1 0 0 1,493 596 412 66 0 68 126 579 560 10 Model Type Logisti c Logistic Expone ntial Expon ential Linear Linear None Expone ntial Linear Expone ntial Expone ntial Expone ntial Logistic Exponent ial Exponent ial Exponent ial Exponent ial Exponent ial Exponent ial Expone ntial Expone ntial Expone ntial Expone ntial Expone ntial LogisticExponenti al Years Used All All All All ( 1982

2000) ( 1982

2000) ( 1982 2000) ( 1982

2000) ( 1978

2000) ( 1982

2000) ( 1977

2000) All All All All All All All All All ( 1977

2000) ( 1989

2000) ( 1989

2000) ( 1989

2000) All Model FitInterce pt ( c) 7.91 29.02

1,433,27 8

1,726,5 74 129.24

176,358 22.51  2.76 9.77 49.68 99.95 99.98 3.04 17.84 4.59 20.72 116.08 14.84 27.96 5.89 25.80 Parameter s Slope ( m)

0.00192 1

0.0125 51 727.82 877.78

0.06326 6 91.36

0.00960 9 0.00256 2

0.00367 4

0.024147

0.049194

0.049252 0.000065

0.007347

0.000964

0.00921 9

0.05757 6

0.00634 2

0.01261 7

0.00152 6  0.012105 Todd Sherwood August 25, 2003 Page 15 Appendix B. Tables of Sales Projections Data   Alternate Case Ag Tractor Combines Windro wers Balers Sprayers Irrigation Sets Other Ag Year 10< x# 2 5 25< x# 5 0 50< x# 7 5 75< x# 1 00 100< x#

175 > 175 75< x# 1 00 100< x#

175 > 175 All All 10< x# 1 00 > 100 10< x# 25 25< x# 50 50< x# 75 75< x# 100 100< x# 17 5 > 175 10< x# 5 0 50< x# 7 5 75< x# 1 00 100< x#

175 175< x#

300 > 300 Historica l 1973 0 13,805 11,388 9,698 30,545 8,097 595 3,584 2,633 718 15 42 118 14 302 182 744 779 209 230 86 17 Data 1974 0 13,849 13,703 12,159 35,372 9,725 785 3,568 3,100 983 44 44 137 27 571 160 806 870 376 220 80 35 ( from PSR) 1975 0 9,881 13,442 16,465 43,681 13,399 1,178 4,025 4,557 697 111 62 69 37 668 264 1,078 1,123 578 147 119 49 1976 0 9,713 11,199 18,007 50,847 17,175 1,587 6,307 6,077 208 126 71 65 73 1,316 300 1,229 1,567 746 197 147 51 1977 0 8,816 9,993 20,038 60,145 28,152 1,133 13,456 8,288 736 194 119 80 211 979 468 1,733 2,307 687 292 178 143 172 1978 0 7,715 9,551 18,061 53,740 26,933 778 14,017 8,201 3,447 313 144 130 90 187 717 2,282 4,198 624 564 261 219 151 1979 2 11,643 12,175 20,207 57,395 33,776 1,446 14,557 8,456 2,828 308 197 160 158 480 802 2,912 5,176 797 653 268 290 173 1980 1,091 21,630 20,778 20,105 44,232 28,052 1,651 10,456 7,868 3,678 118 617 227 218 286 719 3,087 4,765 910 583 168 259 147 11 1981 1,088 22,852 18,323 19,367 29,834 22,544 1,510 9,630 7,712 3,924 264 609 130 182 280 685 2,468 3,673 555 450 564 226 122 15 1982 1,087 25,484 17,201 17,903 24,551 20,394 964 6,509 6,653 2,007 263 555 122 127 294 512 1,851 2,756 427 381 634 245 125 17 1983 1,089 26,396 16,231 15,833 18,682 17,396 808 5,748 6,026 1,887 296 612 130 169 262 506 1,745 2,499 357 304 604 242 129 16 1984 1,883 25,788 15,470 14,478 16,075 16,246 1,263 6,471 5,830 2,082 142 344 139 140 358 429 1,427 2,196 315 277 257 144 103 3 1985 2,045 24,169 14,535 12,656 8,346 11,538 758 4,152 3,515 3,304 147 353 154 145 374 352 1,411 2,170 380 293 272 128 74 4 1986 2,191 23,416 13,854 11,369 7,237 6,302 0 4,991 2,339 3,297 189 367 128 141 388 154 1,467 1,864 362 251 115 97 56 0 1987 3,617 21,686 11,963 10,657 8,727 8,107 16 5,999 5,266 4,102 180 336 115 95 228 112 1,350 1,622 418 254 119 94 55 0 1988 3,843 20,171 11,534 10,645 9,600 17,221 13 2,895 4,501 2,725 235 204 627 107 237 110 1,597 1,799 501 277 133 103 59 0 1989 2,605 20,714 11,534 10,565 9,013 23,217 15 2,637 7,669 2,614 212 232 658 118 259 121 1,377 1,687 471 178 4 334 489 504 70 1990 6,314 30,260 9,154 13,420 10,032 30,550 11 2,629 4,444 2,270 307 275 1,125 4 18 11 870 1,030 405 189 0 171 808 735 17 1991 6,951 37,759 9,951 12,346 7,738 21,022 16 2,228 4,253 2,128 234 271 1,617 5 43 39 1,221 1,272 422 200 0 138 740 796 15 1992 6,771 39,100 8,245 8,207 8,811 19,494 10 2,119 4,095 1,963 228 281 2,063 6 40 42 1,066 1,083 398 200 4 145 733 810 172 1993 7,761 39,983 6,534 5,504 11,091 19,763 9 681 5,316 2,147 229 278 2,336 25 56 42 1,284 1,155 437 211 4 156 686 739 191 1994 7,880 38,761 9,082 5,753 13,881 22,911 7 378 5,092 2,231 237 322 2,508 29 61 46 1,435 1,261 476 220 3 162 750 780 222 1995 8,135 34,458 10,803 6,290 14,657 24,525 3 346 5,542 2,302 225 281 2,580 33 68 54 1,572 1,385 501 233 3 171 828 754 248 1996 8,744 38,158 12,541 7,881 16,392 29,731 5 1,329 7,569 2,273 208 332 2,981 34 71 55 1,575 1,439 517 203 29 168 630 665 473 1997 8,129 42,609 13,723 9,344 28,487 31,362 3 809 7,933 2,280 230 324 4,040 35 73 55 1,577 1,502 532 188 43 161 518 780 463 1998 11,049 41,423 12,685 11,077 31,884 31,603 6 860 8,885 2,018 254 235 4,254 36 75 56 1,622 1,548 549 200 48 147 541 758 559 1999 14,086 45,959 15,687 11,401 26,980 28,846 5 873 5,780 1,918 211 194 4,012 180 50 143 493 643 540 2000 15,522 54,773 16,849 12,271 28,072 20,435 0 989 4,673 1,927 212 204 4,015 179 42 167 499 556 480 Projectio ns 2001 12,545 47,693 11,616 7,971 23,081 29,865 0 443 6,460 1,917 233 264 3,569 23 33 27 1,482 1,366 460 188 7 140 522 687 636 2002 13,182 49,132 11,565 7,744 23,809 30,743 0 383 6,551 1,875 234 261 3,730 22 29 24 1,482 1,343 459 184 6 138 507 685 690 2003 13,819 50,572 11,514 7,523 24,537 31,621 0 331 6,643 1,834 235 259 3,890 20 26 21 1,482 1,320 458 180 6 136 493 682 744 2004 14,455 52,011 11,463 7,309 25,265 32,499 0 286 6,734 1,794 237 257 4,051 19 23 19 1,483 1,298 457 176 5 134 479 680 797 2005 15,092 53,451 11,412 7,101 25,992 33,376 0 248 6,825 1,755 238 255 4,212 18 21 17 1,483 1,276 456 172 4 132 465 678 851 2006 15,729 54,890 11,362 6,898 26,720 34,254 0 214 6,917 1,717 239 253 4,372 17 19 15 1,483 1,255 455 169 4 130 452 675 905 2007 16,366 56,329 11,312 6,702 27,448 35,132 0 185 7,008 1,679 241 251 4,533 16 17 14 1,483 1,234 454 165 3 128 439 673 958 2008 17,002 57,769 11,262 6,511 28,176 36,010 0 160 7,100 1,642 242 248 4,693 15 15 12 1,484 1,213 453 162 3 126 426 671 1,012 2009 17,639 59,208 11,212 6,325 28,904 36,888 0 138 7,191 1,606 244 246 4,854 15 13 11 1,484 1,193 452 158 3 125 414 668 1,066 2010 18,276 60,648 11,163 6,145 29,631 37,765 0 119 7,282 1,571 245 244 5,015 14 12 10 1,484 1,173 451 155 2 123 402 666 1,120 2011 18,913 62,087 11,114 5,970 30,359 38,643 0 103 7,374 1,537 247 242 5,175 13 11 9 1,484 1,153 450 152 2 121 391 663 1,173 2012 19,550 63,527 11,065 5,800 31,087 39,521 0 89 7,465 1,503 248 240 5,336 12 9 8 1,484 1,134 449 148 2 119 379 661 1,227 2013 20,186 64,966 11,016 5,635 31,815 40,399 0 77 7,556 1,470 250 238 5,496 12 8 7 1,485 1,115 448 145 2 117 369 659 1,281 2014 20,823 66,406 10,967 5,474 32,543 41,276 0 67 7,648 1,438 251 236 5,657 11 8 6 1,485 1,096 447 142 1 116 358 657 1,334 2015 21,460 67,845 10,919 5,318 33,271 42,154 0 58 7,739 1,407 253 234 5,818 10 7 5 1,485 1,078 446 139 1 114 348 654 1,388 2016 22,097 69,285 10,871 5,167 33,998 43,032 0 50 7,830 1,376 254 232 5,978 10 6 5 1,485 1,060 445 136 1 112 338 652 1,442 2017 22,733 70,724 10,823 5,020 34,726 43,910 0 43 7,922 1,346 256 230 6,139 9 5 4 1,486 1,042 444 134 1 111 328 650 1,495 2018 23,370 72,164 10,775 4,877 35,454 44,788 0 37 8,013 1,316 257 228 6,300 9 5 4 1,486 1,024 443 131 1 109 319 647 1,549 2019 24,007 73,603 10,727 4,738 36,182 45,665 0 32 8,105 1,287 259 226 6,460 8 4 3 1,486 1,007 442 128 1 108 310 645 1,603 2020 24,644 75,043 10,680 4,603 36,910 46,543 0 28 8,196 1,259 260 224 6,621 8 4 3 1,486 990 441 125 1 106 301 643 1,656 2021 25,281 76,482 10,633 4,472 37,637 47,421 0 24 8,287 1,232 262 223 6,781 8 3 3 1,486 974 440 123 1 105 292 641 1,710 2022 25,917 77,921 10,586 4,344 38,365 48,299 0 21 8,379 1,205 263 221 6,942 7 3 2 1,487 957 439 120 0 103 284 638 1,764 2023 26,554 79,361 10,539 4,221 39,093 49,177 0 18 8,470 1,178 265 219 7,103 7 3 2 1,487 941 438 118 0 102 276 636 1,817 2024 27,191 80,800 10,493 4,100 39,821 50,054 0 16 8,561 1,153 266 217 7,263 6 2 2 1,487 925 437 115 0 100 268 634 1,871 Todd Sherwood August 25, 2003 Page 16 Appendix B. Tables of Sales Projections Data   Alternate Case ( continued)

Ag Tractor Combines Windrow ers Balers Sprayers Irrigation Sets Other Ag Year 10< x# 25 25< x# 50 50< x# 75 75< x# 10 0 100< x# 1 75 > 175 75< x# 10 0 100< x# 1 75 > 175 All All 10< x# 10 0 > 100 10< x# 25 25< x# 50 50< x# 75 75< x# 100 100< x# 175 > 175 10< x# 50 50< x# 75 75< x# 10 0 100< x# 1 75 175< x# 3 00 > 300 2025 27,828 82,240 10,446 3,984 40,549 50,932 0 13 8,653 1,127 268 215 7,424 6 2 2 1,487 910 436 113 0 99 260 632 1,925 2026 28,464 83,679 10,400 3,870 41,276 51,810 0 12 8,744 1,103 269 213 7,585 6 2 2 1,488 895 435 110 0 97 253 629 1,978 2027 29,101 85,119 10,354 3,760 42,004 52,688 0 10 8,835 1,079 271 212 7,745 5 2 1 1,488 880 434 108 0 96 245 627 2,032 2028 29,738 86,558 10,309 3,653 42,732 53,565 0 9 8,927 1,055 273 210 7,906 5 2 1 1,488 865 433 106 0 94 238 625 2,086 2029 30,375 87,998 10,263 3,549 43,460 54,443 0 8 9,018 1,032 274 208 8,066 5 1 1 1,488 850 432 104 0 93 232 623 2,139 2030 31,012 89,437 10,218 3,448 44,188 55,321 0 6 9,110 1,009 276 206 8,227 5 1 1 1,488 836 431 101 0 92 225 621 2,193 2031 31,648 90,877 10,173 3,349 44,916 56,199 0 6 9,201 987 278 205 8,388 4 1 1 1,489 822 430 99 0 90 218 618 2,247 2032 32,285 92,316 10,128 3,254 45,643 57,077 0 5 9,292 966 279 203 8,548 4 1 1 1,489 808 429 97 0 89 212 616 2,300 2033 32,922 93,756 10,083 3,161 46,371 57,954 0 4 9,384 945 281 201 8,709 4 1 1 1,489 795 428 95 0 88 206 614 2,354 2034 33,559 95,195 10,039 3,071 47,099 58,832 0 4 9,475 924 282 199 8,870 4 1 1 1,489 781 427 93 0 86 200 612 2,408 2035 34,195 96,635 9,994 2,984 47,827 59,710 0 3 9,566 904 284 198 9,030 3 1 1 1,490 768 426 91 0 85 194 610 2,461 2036 34,832 98,074 9,950 2,899 48,555 60,588 0 3 9,658 884 286 196 9,191 3 1 1 1,490 755 425 89 0 84 189 608 2,515 2037 35,469 99,513 9,906 2,816 49,282 61,465 0 2 9,749 865 288 194 9,351 3 1 0 1,490 743 424 87 0 83 183 606 2,569 2038 36,106 100,953 9,863 2,736 50,010 62,343 0 2 9,840 846 289 193 9,512 3 0 0 1,490 730 423 86 0 82 178 603 2,623 2039 36,743 102,392 9,819 2,658 50,738 63,221 0 2 9,932 827 291 191 9,673 3 0 0 1,490 718 422 84 0 80 173 601 2,676 2040 37,379 103,832 9,776 2,582 51,466 64,099 0 2 10,023 809 293 190 9,833 3 0 0 1,491 706 421 82 0 79 168 599 2,730 2041 38,016 105,271 9,733 2,509 52,194 64,977 0 1 10,115 791 294 188 9,994 2 0 0 1,491 694 421 80 0 78 163 597 2,784 2042 38,653 106,711 9,690 2,437 52,922 65,854 0 1 10,206 774 296 186 10,155 2 0 0 1,491 682 420 79 0 77 159 595 2,837 2043 39,290 108,150 9,647 2,368 53,649 66,732 0 1 10,297 757 298 185 10,315 2 0 0 1,491 671 419 77 0 76 154 593 2,891 2044 39,926 109,590 9,604 2,300 54,377 67,610 0 1 10,389 740 300 183 10,476 2 0 0 1,492 660 418 75 0 75 150 591 2,945 2045 40,563 111,029 9,562 2,235 55,105 68,488 0 1 10,480 724 301 182 10,636 2 0 0 1,492 649 417 74 0 74 145 589 2,998 2046 41,200 112,469 9,520 2,171 55,833 69,365 0 1 10,571 708 303 180 10,797 2 0 0 1,492 638 416 72 0 73 141 587 3,052 2047 41,837 113,908 9,478 2,109 56,561 70,243 0 1 10,663 693 305 179 10,958 2 0 0 1,492 627 415 71 0 72 137 585 3,106 2048 42,474 115,348 9,436 2,049 57,288 71,121 0 0 10,754 678 307 177 11,118 2 0 0 1,492 617 414 69 0 70 133 583 3,159 2049 43,110 116,787 9,394 1,991 58,016 71,999 0 0 10,845 663 309 176 11,279 2 0 0 1,493 606 413 68 0 69 129 581 3,213 2050 43,747 118,227 9,353 1,934 58,744 72,877 0 0 10,937 648 310 174 11,440 1 0 0 1,493 596 412 66 0 68 126 579 3,267 Model Type Linear Linear Exponent ial Exponent ial Linear Linear None Exponent ial Linear Exponent ial Exponent ial Exponent ial Linear Exponentia l Exponentia l Exponentia l Exponentia l Exponentia l Exponentia l Exponent ial Exponent ial Exponent ial Exponent ial Exponent ial Linear Years Used All All All All ( 1982

2000) ( 1982

2000) ( 1982 2000) ( 1982

2000) ( 1978

2000) ( 1982

2000) ( 1977

2000) All All All All All All All All All ( 1977

2000) ( 1989

2000) ( 1989

2000) ( 1989

2000)

Model Fit Intercept  1,261,65 0

2,832,68 1 7.91 29.02  32.05

1,726,57 4 129.24  176,358 22.51  2.76 9.77  317,836 49.68 99.95 99.98 3.04 17.84 4.59 20.72 116.08 14.84 27.96 5.89  106,774 Parameter s Slope 636.8 1439.5

0.001921

0.012551 0.018179 877.78

0.063266 91.36

0.009609 0.002562

0.003674 160.62  0.024147  0.049194  0.049252 0.000065  0.007347  0.000964

0.009219

0.057576

0.006342

0.012617

0.001526 53.68 Todd Sherwood August 25, 2003 Page 17 Ag Tractor Sales 100 < x < 175hp 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Linear Fit AG Tractor Sales 50 < x < 75hp 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold Sales Exponential Fit Ag Tractor Sales 10 < x < of < 25hp Logistic and Linear Projections 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Logistic Fit Linear Fit Appendix C: Thumbnail Graphs of Projections Todd Sherwood August 25, 2003 Page 18 AG Tractor Sales > 175hp 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Linear Fit AG Tractor Sales 75 < x < 100hp 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Ag Tractor Sales 25 < x < 50hp Logistic, Linear, and Exponential Projections 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 Annual Units Sold PSR Data Logistic Linear Exponential Todd Sherwood August 25, 2003 Page 19 Combine Sales 101< x < 175hp 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Combine Sales > 175hp 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Linear Fit Total Windrower Sales 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Total Baler Sales 125 150 175 200 225 250 275 300 325 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit < 100hp Sprayer Sales and Projections 0 100 200 300 400 500 600 700 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Sprayers > 100hp Logistic and Linear Projections  2,000 0 2,000 4,000 6,000 8,000 10,000 12,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Logistic Fit Series3 Todd Sherwood August 25, 2003 Page 20 Irrigation Sets 100 < x < 175hp 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Irrigation Sets > 175hp 0 100 200 300 400 500 600 700 800 900 1,000 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Irrigation Sets 50 < x < 75hp 0 100 200 300 400 500 600 700 800 900 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Irrigation Sets 75 < x < 100hp 0 500 1,000 1,500 2,000 2,500 3,000 3,500 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Irrigation Sets 10 < x < 25hp 0 20 40 60 80 100 120 140 160 180 200 220 240 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Irrigation Sets 25 < x < 50hp 0 200 400 600 800 1,000 1,200 1,400 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Todd Sherwood August 25, 2003 Page 21 Other Ag Equip 10 < x < 50hp 0 100 200 300 400 500 600 700 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Other Ag Equip 50 < x < 75hp 0 100 200 300 400 500 600 700 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Other Ag Equip 75 < x < 100hp 0 50 100 150 200 250 300 350 400 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Other Ag Equip 100 < x < 175hp 0 100 200 300 400 500 600 700 800 900 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Other Ag Equip 175 < x < 300hp 400 450 500 550 600 650 700 750 800 850 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Units Sold PSR Data Exponential Fit Other Ag Equipment > 300hp Logistic and Linear Projections 0 500 1,000 1,500 2,000 2,500 3,000 3,500 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Annual Unit Sales PSR Data Logistic Fit Series3


[Federal Register Volume 79, Number 58 (Wednesday, March 26, 2014)]
[Notices]
[Page 16793]
[FR Doc No: 2014-06542]

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ENVIRONMENTAL PROTECTION AGENCY

[EPA-HQ-OPP-2013-0676; FRL-9908-29]

Pesticides; Consideration of Spray Drift in Pesticide Risk
Assessment: Extension of Comment Period

AGENCY: Environmental Protection Agency (EPA).

ACTION: Notice; extension of comment period.

-----------------------------------------------------------------------

SUMMARY: EPA issued a notice in the Federal Register of January 29,
2014, concerning the availability of two draft guidance documents for
public comment. This document extends the comment period for 30 days,
from March 31, 2014 to April 30, 2014. The comment period is being
extended to provide additional time for commenters to prepare their
responses.

DATES: Comments, identified by docket identification (ID) number EPA-
HQ-OPP-2013-0676, must be received on or before April 30, 2014.

ADDRESSES: Follow the detailed instructions as provided under ADDRESSES
in the Federal Register document of January 29, 2014.

FOR FURTHER INFORMATION CONTACT: For the ecological risk assessment
guidance document, Faruque Khan, Environmental Fate and Effects
Division, (7507P), Office of Pesticide Programs, Environmental
Protection Agency, 1200 Pennsylvania Ave. NW., Washington, DC 20460-
0001; telephone number:<PHONE_NUMBER>; email address:
<EMAIL_ADDRESS>    For the human health risk assessment guidance document, Jeff
Dawson, Health Effects Division, (7509P), same address; telephone
number:<PHONE_NUMBER>; email address<EMAIL_ADDRESS>
SUPPLEMENTARY INFORMATION: This document extends the public comment
period established in the Federal Register notice of January 29, 2014
(79 FR 4691; FRL-9903-12), which requested comment on two draft
guidance documents. The extension was requested by the National
Agricultural Aviation Association, the Agricultural Retailers
Association, and CropLife America. EPA is hereby extending the comment
period, which was set to end on March 31, 2014, to April 30, 2014.
    To submit comments, or access the docket, please follow the
detailed instructions as provided under ADDRESSES in the January 29,
2014 Federal Register document. If you have questions, consult the
person listed under FOR FURTHER INFORMATION CONTACT.

List of Subjects

    Environmental protection, Administrative practice and procedure,
Agricultural commodities, Pesticides and pests.

    Dated: March 19, 2014.
Marty Monell,
Acting Director, Office of Pesticide Programs.
[FR Doc. 2014-06542 Filed 3-25-14; 8:45 am]
BILLING CODE 6560-50-P


[Federal Register Volume 82, Number 67 (Monday, April 10, 2017)]
[Notices]
[Pages 17326-17327]
[FR Doc No: 2017-07018]

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DEPARTMENT OF TRANSPORTATION

Federal Railroad Administration

[Docket Number FRA-2017-0021]

Notice of Application for Approval of Discontinuance or
Modification of a Railroad Signal System

    Under part 235 of Title 49 of the Code of Federal Regulations (CFR)
and 49 U.S.C. 20502(a), this document provides the public notice that
on March 2, 2017, National Railroad Passenger Corporation (Amtrak)
petitioned the Federal Railroad Administration (FRA) seeking approval
for the discontinuance or modification of a signal system. FRA assigned
the petition Docket Number FRA-2017-0021.
    Applicant: National Railroad Passenger Corporation, Mr. Nicholas J.
Croce III, PE, Deputy Chief Engineer C&S, Acting, 2995 Market Street,
Philadelphia, PA 19104.
    Amtrak seeks to remove the wayside signals on both Main Tracks No.
1 and No. 2 at automatic block points 1572 and 1607 on Amtrak's
Northeast Corridor, New England Division, in Rhode Island. Signals
1572-1 and 1572-2 at block point 1572 will fall within the limits of
the future Liberty interlocking. These signals will be removed due to
the installation of the new Liberty interlocking. The westbound distant
signals to Kingston interlocking at block point 1607, signals 1607-1
and 1607-2 are not required in NORAC Rule 562 territory and cab signals
without fixed automatic block signals will be removed from service.
Block point 1607 will remain in service as a block point without
wayside signals.

    The existing automatic train control and Advanced Civil Speed
Enforcement System (ACSES) designs will be modified to support the
modifications to the block design with the addition of the new Liberty
interlocking. ACSES will enforce a positive stop at each interlocking
and a stop to a train with failed cab signal equipment unless the ``C''
signal is displayed allowing the failed train to enter the block.
    The reason for removal of the signals is to eliminate maintenance
and operation of unnecessary hardware no longer needed, and to reduce
delays to trains caused by failures of the signals.
    A copy of the petition, as well as any written communications
concerning the petition, is available for review online at
www.regulations.gov and in person at the U.S. Department of
Transportation's (DOT) Docket Operations Facility, 1200 New Jersey
Avenue SE., W12-140, Washington, DC 20590. The Docket Operations
Facility is open from 9 a.m. to 5 p.m., Monday through Friday, except
Federal Holidays.
    Interested parties are invited to participate in these proceedings
by submitting written views, data, or comments. FRA does not anticipate
scheduling a public hearing in connection with these proceedings since
the facts do not appear to warrant a hearing. If any interested parties
desire an opportunity for oral comment and a public hearing, they
should notify FRA, in writing, before the end of the comment period and
specify the basis for their request.
    All communications concerning these proceedings should identify the
appropriate docket number and may be submitted by any of the following
methods:
     Web site: http://www.regulations.gov. Follow the online
instructions for submitting comments.
     Fax:<PHONE_NUMBER>.
     Mail: Docket Operations Facility, U.S. Department of
Transportation, 1200 New Jersey Avenue SE., W12-140, Washington, DC
20590.
     Hand Delivery: 1200 New Jersey Avenue SE., Room W12-140,
Washington, DC 20590, between 9 a.m. and 5 p.m., Monday through Friday,
except Federal Holidays.
    Communications received by May 25, 2017 will be considered by FRA
before final action is taken. Comments received after that date will be
considered if practicable.
    Anyone can search the electronic form of any written communications
and comments received into any of our dockets by the name of the
individual submitting the comment (or signing the document, if
submitted on behalf of an association, business, labor union, etc.).
Under 5 U.S.C. 553(c), DOT solicits comments from the public to better
inform its processes. DOT posts these comments, without edit, including
any personal information the commenter provides, to
www.regulations.gov, as described in the system of records notice (DOT/
ALL-14 FDMS), which can be reviewed at https://www.transportation.gov/privacy. See also https://www.regulations.gov/privacyNotice for the
privacy notice of regulations.gov.

    Issued in Washington, DC.
Robert C. Lauby,
Associate Administrator for Railroad Safety, Chief Safety Officer.
[FR Doc. 2017-07018 Filed 4-7-17; 8:45 am]
 BILLING CODE 4910-06-P