United States 

Department of

Agriculture 5/4 Ponderosa Pine

Forest Service

Forest

Products Shop Grade Cutting

Laboratory

Research

Paper

Yields

FPL 394 
Abstract

Improved utilization of western lumber for factory 

usage is possible, using recently developed cutting 

yields for Shop grades of ponderosa pine. Relative 

value differences between lumber grades are computed 

from a comprehensive data base representative of the 

raw material consumed by the moulding and millwork 

industries.

Keywords: Softwood lumber, Multiple rip, Millwork 

yields, clear cutting yields, lumber cut-up, computersimulated processing, utilization.

Acknowledgement

The participation, assistance, and expertise received in 

this study from the Timber Quality Research Project of 

the Pacific Northwest Forest and Range Experiment 

Station is respectfully acknowledged. 
United States 

Department of 

Agriculture

Forest Service 

Forest

Products

Laboratory’

Research

Paper

FPL 394 

June 1981 

Introduction

5/4 Ponderosa Pine 

Shop Grade Cutting

Yields

By

KENT A. MC DONALD, Forest Products Technologist

PAMELA J. GIESE, Computer Programmer

and

RICHARD O. WOODFIN, Forest Products Technologist

Pacific Northwest Forest and Range Experiment

Station, Portland, Oreg. 

The moulding and millwork industries that thrive on 

western lumber consume nearly 1.3 billion board feet of 

ponderosa pine annually. They are long-time, wellestablished industries that have developed traditional 

and time-honored practices of purchasing lumber, 

remanufacturing it, and marketing the resulting product. The moulding and millwork industries produce a 

wide range of products that include the long, clear 

pieces of trim used to finish off rooms in a house, and 

headers, jambs, and parts that make up doors, to the 

variety of frames, sash, and sills that go into window 

units. All these industries are faced with the problem 

(and the necessity) of achieving the maximum product 

utilization possible from every piece of raw material. 

Over time, these industries developed their own utilization rules-of-thumb and cutting yield guidelines, and 

used them for making lumber purchases based on predictable supplies, product markets, and acceptable profit margins. As the profit margins narrowed, new products and utilization practices were developed, such as 

fingerjointing, to make use of short pieces in the product line and decrease waste. 

The supplying sawmills and the moulding and millwork 

industries became accustomed to marketing and purchasing Shop grade lumber in a No. 3 and Better mix. A 

new and major problem to these industries has come 

about as a result of the ponderosa pine timber supply 

changing to smaller, lower quality trees. The result has 

been larger volumes of lower grade lumber that have a 

lower utility, and yield shorter and/or narrower pieces. 

Twenty years ago the No. 3 and Better mix included 

1

 Maintained at Madison, Wis., in cooperation with the University of 

Wisconsin.

around 5 to 10 percent No. 3 Shop. Recently, the No. 3 

Shop grade represents 35 to 40 percent, with some 

shipments as high as 80 to 85 percent No. 3 Shop. 

The problem arises with continued use of the rule-ofthumb and yield guidelines after the grade mix 

changes. The rules are then no longer viable tools for 

making adequate decisions for lumber purchasing, processing, and product marketing. 

These industries need basic cutting yield information 

by lumber grade to make essential manufacturing decisions and keep pace with the constantly changing raw 

material supply. 

Without cutting yield information by lumber grade, mill 

managers may resort to measuring worker productivity 

in a mill by estimating the amount of waste that is left 

over after processing. This practice, of itself, can account for losing high-value cuttings and getting low 

yields of large cuttings, because the waste factor is 

lower. For example, judging an operator on his ability 

to utilize the full width of a board just to minimize 

waste causes him to favor several narrow rip widths instead of fewer wide rip widths. 

Cutting yields are also needed by management when 

facing production decisions that involve alternative products or processes where the grade of the raw material 

will have a controlling effect on product output. An example of this need is the prospect of installing fingerjointing and/or edge gluing equipment that can convert 

lower grades of raw material into a product that may be 

competitive with solid wood products. Cutting yields by 

grade were obtained by sampling the 5/4 ponderosa 

pine, No. 3 and Better Shop lumber resource, measuring and recording all defects, processing the data by a 

computer model designed to simulate the cutting of in-
dividual boards, and finally summarizing the cutting 

results by each lumber grade. 

The moulding and millwork industries are typically rip 

first, then crosscut operations, with an additional rerip 

capability for salvage. Normally, all cuttings produced 

are less than 100 percent clear of defect. Each plant 

will have a different set of guidelines that permit varying degrees of defect in certain locations of a cutting 

when the utility of the final product is not affected. To 

avoid this practice as a problem to this study, only 

clear cutting yields were calculated to provide conservative cutting yield information that is equally usable 

by everyone. 

Study Procedures 

Sampling

The first objective of this study was to obtain a data 

base representative of the 5/4, No. 3 and Better Shop 

ponderosa pine lumber being consumed by the 

moulding and millwork industry. 

Sample lumber was obtained over the geographical 

range of ponderosa pine (fig. 1). Mill cooperators 

selected the study lumber from their inventories that 

they judged to be representative of their suppliers. No 

attempt was made to sample for variation of sawing 

methods.

figure 1.—Distribution of ponderosa pine. 

The three cooperating mills were located in Oregon, 

California, and Arizona. At the Oregon site, study 

material was supplied from the states of Washington, 

Idaho, and Oregon. At the California and Arizona sites, 

samples were selected from suppliers within their 

respective states. 

All the 5/4 Shop lumber sampled was collected in increments of a standard shipping unit, defined as a 

strapped, dry, solid-stacked pile of lumber. These units 

usually contain about 2,500 board feet, depending upon 

the length, width, and number of boards. A representative mix of lengths was included in the sample selection.

Approximately 53,000 board feet of No. 3 and Better 

Shop lumber were selected for sample material from 

the three subareas. The distribution of boards sampled 

by Shop grade is shown in table 1. 

All sample material was reinspected by Quality Supervisors of the Western Wood Products Association to 

verify the grade and scale. Individual boards determined by the inspector to be misgraded were changed 

to the correct grade. All boards were numbered and 

marked with grade, gross surface measure, and net surface measure. Board measure was obtained for the 5/4 

stock by multiplying surface measure by 1.25. 

Data Collection

A complete digital record was made of the board and 

all defects, including the type of defect and its location 

to the nearest 1/4 inch. Board data recorded include 

board number, grade, unit number, width, length, gross 

surface measure, and net surface measure. 

Defects were measured to the nearest 1/4 inch on both 

sides of each board using measuring tables constructed for this purpose (fig. 2). Defects were tallied by 

type and four coordinate points of a quadralateral (fig. 

3).

All wood area was classified as either defect or clear. 

Any blemish not acceptable in a clear cutting was 

classified as defect and recorded. 

Figure 2.—Board measuring procedure. (M 148 959) 

2
Figure 3.—Defect measuring procedure. 

(M 148 955, M 148 954) 

For overlapping defects, the entire area was recorded 

and the predominant defect code (fig. 4) was assigned. 

All material sampled was purchased “oversize” in 

thickness so defect boundaries had to be positioned 

where the defect was expected to be, following final 

surfacing. This was particularly important when determining the extent and severity of torn or chipped grain 

that might be “dressed out” or even reoccur following 

subsequent machining. 

Simulated Board Processing Program (OPTYLD) 

A computer program that would select the best sawing 

solution for each study board was developed by modeling the board cutting procedure typical of the moulding 

and millwork industry. 

The three basic sawing operations typically used to 

produce the desired products and thus modeled in the 

computer program are the multiple rip, the crosscut, 

and the rerip (fig. 5). Normally, under operator control, 

cutting decisions are based on the size, location, 

number, and quality of potential cuttings available in 

the piece, as well as the cuttings desired by mill 

management. At the multiple rip, the operator decides 

what, where, and how many widths will be obtained

from each board, and where the rip saws are to be set

to edge and rip the board. The crosscut operators

receive the ripped widths, determine the length cut to

be made, and crosscut the piece. These operators

decide what, if any, defect remains in the cutting and

which pieces should be reripped. The operator of the

rerip decides the maximum width of cutting that can be

salvaged and what, if any, defect will be permitted.

The computer model combines the board and defect

data of both board faces, and selects the sawing solution that results in the highest value and/or yield of

clear cuttings. To obtain the best sawing solution, all

possible combinations of rip widths are calculated,

comparing only the value or yield of the clear cuttings

obtained from the ripping and crosscutting process.

After the best sawing solution is selected, the remaining board areas are assessed for additional rerip yield.

The computer program was designed to handle several

different options in order to include the entire range of

potential decisions made during the three basic sawing

operations. All of these can have an effect on the final

cutting solution and yield.

The options available in the computer model are:

— size and number of cutting widths obtainable at 

the multiple rip 

— number of rip saws that can be set at any one 

time

— edging allowance to clean up and/or straighten 

one edge of the board 

— cutting lengths specified for specific cutting sizes 

or random for operations with unlimited cutting 

length requirements or finger- jointing capabilities 

— size and number of rerip cutting widths 

— optimization method based either on total cutting 

area or on total relative value of cutting 

Figure 4.—Multiple defect recording. (M 148 957) 

3
Figure 5.—Basic sawing operations. 

— preselection of different relative cutting value indexes by cutting lengths and widths 

A selection of options typical of an industry would be: 

— five cutting widths of 1.75, 2.25, 3.25, 4.25, and 

4.75 inches 

— five settable rip saws for up to five rips 

— edging allowance of 1/4 inch to straighten one 

edge

— random length cuttings greater than 9 inches but 

not to exceed 84 inches 

— four rerip widths of 1.75, 2.25, 3.25, and 4.25 

inches

— optimize cutting value to get highest return from 

each board, based on a cutting value index provided.

Constraints of the computer model used to analyze the 

data are: 

— only clear, two-face cuttings can be obtained 

— 1/4-inch increments used to describe board size, 

defect size, saw kerf, and cutting dimensions 

— maximum board size of 24 inches in width and 16 

feet in length. 

The data for each board are processed by this computer model for the best solution, after which the size 

and location of all clear cuttings are known (fig. 6). 

Tabulation is then possible by board, by grade, and by 

size class, or collectively to represent a grade mix of 

No. 3 and Better Shop. 

This program, OPTYLD, is written in FORTRAN V, 

documented and explained in another publication.2

Sample Data 

The 53,000 board feet of 5/4, No. 3 and Better Shop 

lumber sampled ranged in length from 5 feet to 16 feet. 

Approximately 73 percent of the sample (39,000 board 

feet) was 16 feet in length. Sample board widths ranged 

from 4 to 24 inches, with approximately one-third of the 

2

 Giese, P. J., and K. A. McDonald, 1981. OPTYLD—A computer program 

to maximize cutting yields. To be published as an FPL Research Paper. 

4

(M 148 960) 

total volume being less than 11 inches, one-third between 11 and 14 inches, and the remaining one-third 15 

to 24 inches wide (table 2). The frequency distribution 

by 1-inch width classes (actual dry width in whole 

inches) shows a tendency toward manufacturing on the 

2- inch multiple when width is less than 13 inches, and 

random when 13 inches and wider (fig. 7). Boards 

shorter than 16 feet were predominantly 12 inches wide 

or less. 

These distributions of widths and lengths of the sample lumber are representative of the lumber being shipped to the moulding and millwork industries. 

Methods and Results 

Cutting Yield by Grade 

With the program options available and the given constraints, the computer program determines individual 

yield results several different ways, depending upon the 

specific interest and/or intended application. The decision, for this study, was made to compute the highest 

value yield for each board in the study using the options as follows: 

— five rip saws 

— rip to five widths — 1/4- inch edging allowance — random length cuttings of 9 to 84 inches long. 

The ripping widths selected were 1.75, 2.25, 3.25, 4.25, 

and 4.75 inches. The value index used for cutting length 

and width ranges from a low value of 780 to a high of 

1300 (table 3). Selection of these options and values 

came from industry cooperators and is assumed to be 

representative. Note that these values are not 

necessarily dollars.

After computing each board for the highest value yield, 

resulting board information consists of the total clear 

cutting area, total value, percent clear area, and the 

sawing solution. Each clear cutting size and value is 

also known. 
(a) sample board M 148 958 

(b) computer image 

1. 01306 XX SHOP 5/4 PONDEROSA PINE DEFECT TOTAL 19 00 000 47 677 01

2. 01306 + + 14 14 6/4 POND PINE 11 3 14 SAMPLE BOARD UNIT 013225 02

3. 01306 W N 0 0 — 000 30 — 008 1 03

4. 01306 WN00 04 238 00 05 290 N K 0 0 — 290 16 — 334 WN43 44 089 44 47 113 1 05

5. 01306 WN00 13 000 00 02 118 SP40 47 113 37 47 261 SP37 47 261 40 47 323 1 06

6. 01306 SP30 — 323 47 — 362 SP31 47 362 36 47 422 SP36 — 422 37 — 497 1 07

7. 01306 SP32 47 497 30 47 513 SP30 47 513 33 47 560 SP33 47 560 36 47 563 1 08

6. 01306 NK07 — 406 16 — 425 NK29 — 609 39 — 624 SP28 47 624 18 47 643 2 09

9. 01306 SP31 47 643 36 47 658 NK18 — 658 47 — 677 SC12 — 673 18 — 677 2 10

(c) board data 

(d) intermediate solution; cutting yield = 48 percent, value = 6.0 

(e) best sawing solution; cutting yield = 57 percent, value = 8.2. M 149 884 

Figure 6.—Sample board (a) showing computer image (b), of board data (c), with one intermediate sawing 

solution (d), and the best sawing solution (e). 

5
Figure 7.—Width-length distribution.

The individual board cutting yields can be summarized 

by any desired grouping, but are usually by lumber 

grade. From the yield tables presented (tables 4-8), the 

production, purchasing, processing, and alternative 

product-line decisions can be evaluated by mill 

operators and managers, keeping in mind the limitations involved. A summary table by grade (table 4) 

shows actual numbers of cuttings from all boards by 

cutting width and length classes consistent with the 

cutting value index table. These are the maximum value 

cutting yields that could be obtained from these boards 

under the conditions, options, and constraints of this 

particular computer run. These cutting yields are not 

necessarily the maximum volume of cutting, as each 

board solution was based on maximizing value. 

Summarizing actual cutting value for each grade by 

cutting width and length classes reflects the relative 

recovery by cutting size (table 5). In conjunction with 

table 4, the relative importance of the few large, highvalue cuttings as compared to the numerous small, 

low-value cuttings can easily be recognized. The total 

value for a grade is important when comparing revised 

computer programs and different cutting options using 

the same board data base. 

The number of cuttings calculated per 1000 square feet 

surface measure (table 6) and per 1000 board feet (table 

7) allows the comparison of yields between grades on 

an equal basis. Comparison can most easily be made 

by studying the tables. However, note the relatively 

even distribution of nearly all cutting sizes for No. 1 

Shop, the decrease in the long, wide cuttings for No. 2 

Shop, and the sharp increase in the short, narrow cuttings for No. 3 Shop. For example, 20.1 of the largest 

cuttings computed (4.75 by 84 in.) were recovered from 

each 1000 board feet of No. 1 Shop (table 7), whereas 

only 1.4 came from the same volume of No. 3 Shop. The 

number of pieces 3.25 inches wide by 36 to 47 inches 

long recovered from 1000 board feet was more nearly 

equal, ranged only from 19.0 for No. 1, to 19.3 for No. 2, 

to 16.6 for No. 3 Shop. Whereas these results are quite 

specific and known for the first time, they verify the 

previously suspected differences believed to exist between the Shop grades, as they are a reflection of the 

grading rules. 

The volumes and percentages of cuttings recovered by 

grade can be obtained from the table that shows total 

cutting area as a percent of total board area (table 8). 

Cutting volumes expected by grade can be calculated 

from these percentages. This table can be used to compare the distribution of required cuttings needed by an 

operation against what a grade can yield with proper 

decisions.

Cutting Yield by Board Size 

With board size in the ponderosa pine data base and 

cutting yield by board, the opportunity exists to look at 

the yields by board size for the three Shop grades. The 

yield of clear cuttings from any given board depends on 

the size and location of clear areas and the size of the 

board. Although grading tends to stratify boards by taking these factors into account, there still is concern for 

the existing trend to smaller boards as a result of increased harvest of the second-growth, smaller diameter 

timber resource. 

The percent of cutting yield shows relatively little difference as surface measure (board size) changes within 

each grade. Individual board percentages and average 

percentage plotted for each surface measure show the 

total variation in the board data and how the variation 

increases from No. 1 to No. 3 Shop (figs. 8-10). 

The total board value, plotted against surface measure, 

however, reflects the direct correlation between lowvalue small boards and high-value large boards as a 

result of size and number of cuttings that can be obtained. Individual board values and average value plotted for each surface measure show the total variation 

by board and the trend (figs. 11-13). 

Recovery

The relative difference that occurs betweens Shop 

grades of lumber is evident from the values obtained 

per unit area of cuttings, the recovery percentages, and 

the calculated values per unit of clear cuttings as are 

6
Figure 8.—Yield distribution-No. 1 Shop, 5/4 ponderosa pine. Figure 9.—Yield distribution-No. 2 Shop, 5/4 ponderosa pine. 

(M 149 526) (M 149 524)

Figure 10.—Yield distribution—No. 3 Shop, 5/4 ponderosa Figure 11.—Value distribution-No. 1 Shop, 5/4 ponderosa 

pine. pine.

(M 149 526) (M 149 527)

7
Figure 12.—Value distribution—No. 2 Shop, 5/4 ponderosa 

pine.

(M 149 525) 

shown in table 9. Because the raw material for the 

moulding and millwork industry is purchased on a per 

1000 board feet, the resulting cutting values obtained 

from the study boards are shown based on the same 

measure.

The value per unit of clear cuttings obtained for each 

grade is calculated by: 

(1)

where V = total value of clear cuttings 

A = total board area in clear cuttings (ft2

)

UV = value per unit of clear cutting 

For each grade, these values are 1.007, 0.956, and 0.894 

for No. 1, No. 2, and No. 3 Shop, respectively. These 

values do not vary greatly but do reflect the cutting 

sizes and cutting values obtained by grade. 

Recovery of clear cuttings relative to the board footage 

in the sample is obtained by: 

(2)

where A = total board area in clear cuttings (ft2)

B = board feet 

R = percent recovery of cutting area to board feet 

By grade, recovery percents are 59.3, 55.1, and 47.6 for 

No. 1, No. 2, and No. 3 Shop, respectively. These 

percentages reflect differences due to grading rules applied by the grading association. 

Figure 13.—Value distribution—No. 3 Shop, 5/4 ponderosa 

pine.

(M 149 529) 

Finally, the cutting value expected for 1000 board feet 

is calculated by: 

(3)

where R = percent recovery (from (2)) 

UV = value per unit of clear cutting (from (1)) 

V/M = value per 1000 board feet 

By grades No. 1, No. 2, and No. 3 Shop, the cutting 

values per 1000 board feet are 598, 527, and 425 respectively. Assuming the relative value index table used was 

reasonably accurate, these cutting values should represent the absolute differences in yields between these 

grades. The actual value recovered by an individual 

operation may be substantially different from these 

figures, due to processing methods and acceptance of 

other than clear cuttings. 

Effect of Reripping 

In most millwork and moulding operations, the primary 

breakdown of lumber to obtain clear cuttings involves 

ripping lumber full length followed by crosscutting. 

After crosscutting, all material is then either a clear 

cutting, or classed as defective. The defective material 

may contain clear areas that meet or exceed the 

minimum clear cutting size but were unobtainable by 

the rip, then crosscut, operations. These areas can be 

salvaged by an additional ripping operation, called 

reripping, or backripping. Usually the crosscut operator 

identifies the salvage pieces, cuts them to length, and 

sends them to the rerip operation. 

8
Results presented so far in this report have been the 

combined rip and rerip cutting yields. The reripping 

results are shown as the number of rerip cuttings obtained per 1000 board feet by cutting size (table 10). 

These results show the importance of the reripping 

operation. The additional yield from reripping ranges 

from 2.8 to 5 percent of the total recovered clear cutting area (table 11). Value of the rerips similarly ranges 

from 2.4 to 4.3 percent of total clear cutting value. 

Summary and Discussion 

Completion of this yield study leaves three distinct accomplishments- (1) cutting yields and values, (2) extensive board data base, and (3) an iterative computer 

board processing technique. 

Clear cutting yields and values summarized for 5/4 

ponderosa pine by the three Shop grades make it possible to: identify potential processing improvements; 

evaluate future trends or expectations in lumber supply; 

coordinate production requirements with lumber grade 

input; and identify the actual differences that exist between lumber grades. 

A potential processing improvement might be following 

a management decision to fingerjoint more or less 

stock that would require cutting up a different grade 

mix. Future expectations in lumber supply indicate a 

trend to narrower and shorter No. 3 and Better Shop 

lumber, which will have an economic effect on the size 

and value of clear cuttings produced. The quick and 

timely decisions about the lumber grade input, based 

on product requirements and optimum machine usage, 

need to be based on the cutting yields and values by 

grade. It is not productive to cut up No. 3 Shop lumber 

if short or narrow cuttings are not in demand or if the 

fingerjointing machinery is not operational. 

Some of the decisions that can be made now that cutting yields and values by grade are available, come 

from knowing the cutting yield and value differences as 

reported between grades. This knowledge could result 

in a mill operator’s decision to forego the common 

practice of processing a mixture of No. 3 and Better 

Shop lumber, and instead process a straight No. 3 

Shop and put the No. 1 and No. 2 Shop back on the 

market for a higher return. 

An extensive data base that represents 5/4 No. 3 and 

Better Shop grades of ponderosa pine lumber is behind 

the yield and value tables published herein. Further 

uses of this data base are possible, such as permitting 

the simulated processing of the same boards for direct 

comparisons of yield and/or value results. Other uses 

include determining the effect of the value index as a 

method to control the production yield of certain size 

cuttings, and evaluating alternative processing 

methods such as gang-ripping and fingerjointing. 

Another use is to study the potential benefit of 

automated defect sensing techniques and to evaluate 

the economic effects on yield and value of specific 

defects.

The computer program (OPTYLD), designed to simulate 

the multiple ripping process germane to the softwood 

moulding and millwork industry, uses an iterative 

algorithm that evaluates all possible ways to rip individual boards to obtain the highest yield. The potential application of this program for real time processing 

in conjunction with a computer controlled lumber scanning system was incorporated into the program design. 

Application of the computer program is also possible 

when other board data bases are established for the 

purpose of determining yields of other grades and 

species, including hardwoods, in a multiple-rip first process.

Table 1.—5/4 Ponderosa pine Shop sample information 

Average

Grade Number of Total volume

boards

No. 1 386

No. 2 1,537

No. 3 1,361

Combined 3,284

volume per board 

— — — — Board feet1

 — — — — 

8,534 22.1

26,797 17.4

17,985 13.2

53,316 16.2

1

 Scaled net surface measure times 1.25. 

Table 2.—Board size distribution 

Board length Length distribution 

Width <16 feet 16 feet Total <16 feet 16 feet Total

In. — — — Board feet — — — — — — — Pct — — — —

4-10 7,981 9,590 17,571 15 18 33

11-14 4,591 14,326 18,917 9 27 35

15-24 1,861 14,967 16,828 3 28 32

Total 14,433 38,883 53,316 27 73 —

Table 3.—Relative cutting value index1

Length (in.)

Width 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-83 84

1.75 780 790 800 820 840 860 880 1000 1050

2.25 800 810 820 840 860 880 1000 1030 1150

3.25 810 830 840 860 880 910 1040 1070 1200

4.25 820 840 850 870 890 930 1080 1120 1250

4.75 825 845 855 875 895 950 1100 1145 1300

1

 Values have no units. 

9
Table 4.—Total clear cutting yield-piece count, 5/4 ponderosa 

pine

Width Length (in.)

(in.) 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-63 64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NO. 1 SHOP

386 BOARDS, SURFACE MEASURE = 6827 SQUARE FEET

— — — — — — — — No. of cuttings — — — — — — — — 

1.75 244 286 194 175 184 138 56 74 104

2.25 214 261 160 175 164 96 95 67 173

3.25 198 225 153 177 162 91 83 73 156

4.25 101 177 99 109 100 64 61 60 129

4.75 118 173 117 130 102 87 62 52 171

NO. 2 SHOP 

1537 BOARDS, SURFACE MEASURE = 21,438 SQUARE FEET 

— — — — — — — — No. of cuttings — — — — — — — — 

1.75 1439 1606 1087 980 953 646 286 303 310

2.25 1247 1438 923 862 758 519 435 281 581

3.25 936 1046 680 664 516 324 313 218 430

4.25 355 452 264 262 203 111 138 81 154

4.75 232 404 243 212 143 97 111 91 203

NO. 3 SHOP 

1361 BOARDS, SURFACE MEASURE = 14,388 SQUARE FEET 

— — — — — — No. of cuttings — — — — — — 

1.75 2065 2236 1405 1189 938 516 198 189 165

2.25 1341 1531 1001 869 685 330 278 150 227

3.25 634 812 464 415 301 169 133 73 103

4.25 114 130 85 60 37 22 17 15 28

4.75 95 117 74 46 27 21 26 13 25

Table 5.—Total clear cutting values, 5/4 ponderosa pine 

Width Length (in.)

(in.) 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-83 84 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NO. 1 SHOP

386 BOARDS, TOTAL VALUE1 5100

— — — — — — — — — Value — — — — — — — — — 

1.75 24 43 43 54 78 77 39 69 111

2.25 28 52 47 71 91 71 97 83 261

3.25 39 68 67 106 134 100 127 136 354

4.25 26 71 57 86 106 94 127 155 399

4.75 34 77 76 118 123 145 147 150 615

NO. 2 SHOP 

1537 BOARDS, TOTAL VALUE1

 14,109 

— — — — — — — — — — Value — — — — — — — — — — 

1.75 147 247 245 303 404 360 199 286 332

2.25 169 293 274 351 423 380 444 350 876

3.25 183 314 298 399 425 355 479 408 978

4.25 91 180 152 207 217 162 285 207 477

4.75 67 182 158 189 173 160 262 266 731

NO. 3 SHOP

1361 BOARDS, TOTAL VALUE1

 7648

— — — — — — — — — — Value — — — — — — — — — — 

1.75 210 345 315 366 395 287 138 176 176

2.25 180 311 297 353 377 240 283 186 342

3.25 124 244 203 248 246 185 203 135 234

4.25 30 51 48 47 40 31 35 37 86

4.75 27 53 48 41 32 35 62 37 90

1

 No units. 

10

Table 6.—Cutting yield to surface measure1, 5/4 ponderosa pine 

Width Length (in.)

(in.) 9-12 13-19 20-26 27-35 38-47 48-59 60-71 72-83 84 . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . ... . . . . . . ... . . . . . . . . . . . . . . . . . . . .

NO. 1 SHOP

386 BOARDS, SURFACE MEASURE = 6827 SQUARE FEET

— — — — — Pieces per 1000 ft2

 surf. meas. — — — — — 

1.75 35.7 41.9 28.4 25.6 27.0 20.2 8.2 10.8 15.2 

2.25 31.3 38.2 23.4 25.6 24.0 14.1 13.9 9.8 25.3 

3.25 29.0 33.0 22.4 25.9 23.7 13.3 12.2 10.7 22.8

4.25 14.8 25.9 14.5 16.0 14.6 9.4 8.9 8.8 18.9

4.75 17.3 25.3 17.1 19.0 14.9 12.7 9.1 7.6 25.0 

NO. 2 SHOP 

1,537 BOARDS, SURFACE MEASURE = 21,438 SQUARE FEET 

— — — — — Pieces per 1000 ft2

 surf. meas. — — — — — 

1.75 67.1 74.9 50.7 45.7 44.5 30.1 13.3 14.1 14.5

2.25 58.2 67.1 43.1 40.2 35.4 24.2 20.3 13.1 27.1

3.25 43.7 48.8 31.7 31.0 24.1 15.1 14.6 10.2 20.1

4.25 16.6 21.1 12.3 12.2 9.5 5.2 6.4 3.8 7.2

4.75 10.8 18.8 11.3 9.9 6.7 4.5 5.2 4.2 9.5

NO. 3 SHOP

1,361 BOARDS, SURFACE MEASURE = 14,388 SQUARE FEET

— — — — — Pieces per 1000 ft2 surf. meas.— — — — — — 

1.75 143.5 155.4 97.7 82.6 65.2 35.9 13.8 13.1 11.5 

2.25 93.2 106.4 69.6 60.4 47.6 22.9 19.3 10.4 15.8 

3.25 44.1 56.4 32.2 28.8 20.9 11.7 9.2 5.1 7.2

4.25 7.9 9.0 5.9 4.2 2.6 1.5 1.2 1.0 1.9

4.75 6.6 8.1 5.1 3.2 1.9 1.5 1.8 0.9 1.7

1

 Scaled net surface measure. 

Table 7.—Cutting yield to board measure, 5/4 ponderosa pine 

Width Length (in.)

(in.) 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-83 84 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NO. 1 SHOP

386 BOARDS, BOARD MEASURE = 8534 BOARD FEET

— — — — — — Pieces per 1000 board feet — — — — — — 

1.75 28.7 33.6 22.8 20.6 21.6 16.2 6.6 8.7 12.2

2.25 25.1 30.6 18.8 20.6 19.3 11.3 11.2 7.9 20.3

3.25 23.3 26.4 18.0 20.8 19.0 10.7 9.8 8.6 18.3

4.25 11.9 20.8 11.7 12.8 11.8 7.6 7.2 7.1 15.2

4.75 13.9 20.3 13.8 15.3 12.0 10.3 7.3 6.2 20.1

NO. 2 SHOP 

1537 BOARDS, BOARD MEASURE = 26,797 BOARD FEET 

— — — — — — Pieces per 1000 board feet — — — — — — 

1.75 53.7 59.9 40.6 36.6 35.6 24.1 10.7 11.3 11.6

2.25 46.6 53.7 34.5 32.2 28.3 19.4 16.3 10.5 21.7

3.25 34.9 39.1 25.4 24.8 19.3 12.1 11.7 8.2 16.1 

4.25 13.3 16.9 9.9 9.8 7.6 4.2 5.2 3.0 5.8

4.75 8.7 15.1 9.1 7.9 5.4 3.6 4.2 3.4 7.6

NO. 3 SHOP 

1361 BOARDS, BOARD MEASURE = 17,985 BOARD FEET 

— — — — — — Pieces per 1000 board feet — — — — — — 

1.75 114.8 124.4 78.1 66.1 52.2 28.7 11.0 10.5 9.2

2.25 74.6 85.2 55.7 48.3 38.1 18.4 15.5 8.4 12.6 

3.25 35.3 45.2 25.8 23.1 16.8 9.4 7.4 4.1 5.8

4.25 6.4 7.3 4.8 3.4 2.1 1.3 1.0 .9 1.6

4.75 5.3 6.5 4.1 2.6 1.5 1.2 1.5 .8 1.4
Table 8.—Cutting size distribution, 5/4 ponderosa pine 

Width Length (in.) 

(in.) 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-83 84 . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .

NO. 1 SHOP 

386 BOARDS, TOTAL BOARD AREA = 6,981 SQUARE FEET1

— — — — — Pct cutting area to board area — — — — — 

1.75 0.45 0.80 0.78 0.95 1.33 1.29 0.64 1.00 1.52

2.25 .51 .94 .83 1.22 1.52 1.16 1.40 1.16 3.25

3.25 .70 1.18 1.15 1.77 2.19 1.58 1.76 1.83 4.24

4.25 .46 1.22 .97 1.42 1.72 1.45 1.69 1.99 4.58

4.75 .59 1.31 1.28 1.94 1.98 2.20 1.92 1.88 6.79

NO. 2 SHOP 

1,537 BOARDS, TOTAL BOARD AREA = 22,137 SQUARE FEET1

— — — — — Pct cutting area to board area — — — — — 

1.75 .85 1.42 1.39 1.67 2.18 1.89 1.02 1.29 1.43

2.25 .95 1.63 1.51 1.89 2.23 1.95 2.01 1.54 3.44

3.25 1.02 1.71 1.61 2.10 2.18 1.76 2.08 1.72 3.68

4.25 .51 .97 .81 1.08 2.10 .79 1.19 .84 1.72 

4.75 .37 .97 .84 .98 .87 .76 1.08 1.05 2.54

NO. 3 SHOP 

1,361 BOARDS, TOTAL BOARD AREA = 15,122 SQUARE FEET1

— — — — — Pct cutting area to board area — — — — — 

1.75 1.78 2.89 2.61 2.95 3.11 2.21 1.04 1.17 1.11

2.25 1.49 2.55 2.40 2.79 2.90 1.81 1.87 1.20 1.97

3.25 1.02 1.95 1.60 1.91 1.85 1.34 1.29 .83 1.29

4.25 .24 .41 .38 .36 .30 .22 .21 .22 .46

4.75 .22 .42 .38 .31 .24 .24 .37 .21 .46

1

 Board area determined from board coordinates and is not the 

same as scaled surface measure. 

Table 9.—Cutting value by Shop lumber grade 

R V/M

B V A UV Recovery Cutting 

Shop Total Total Total Value1 cutting value1

grade board value of area of per unit area to per 1,000 

measure cuttings cuttings cutting board board

measure feet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

Board V1 Ft2 V/ft2 Pct

feet

1 8,534 5,100 5,064 1.007 59.3 598

2 26,797 14,109 14,757 .956 55.1 527

3 17,985 7,648 8,559 .894 47.6 425

1 Value has no units. 

Table 10.—Rerip yield to board measure, 5/4 ponderosa pine 

Width Length (in.)

(in.) 9-12 13-19 20-26 27-35 36-47 48-59 60-71 72-83 84 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NO. 1 SHOP

386 BOARDS, BOARD MEASURE = 8534 BOARD FEET

— — — — — Rerip pieces per 1000 board feet — — — — — 

1.75 14.9 10.7 4.2 2.6 2.3 0.8 0.3 0.3 0.6

2.25 12.1 6.5 2.5 2.1 1.8 1.6 1.0 1.0 1.1

3.25 5.9 2.2 1.9 1.3 1.3 .5 .6 .3 .2

4.25 1.5 .9 .5 .3 .4 .1 .1 .2 .1

4.75 — ——— — — — — —

NO. 2 SHOP

1537 BOARDS, BOARD MEASURE = 26,797 BOARD FEET

— — — — — Rerip pieces per 1000 board feet — — — — — 

1.75 18.2 12.0 4.3 1.8 .8 .2 .1 .1 .1

2.25 12.7 6.9 1.8 1.1 .4 .1 .1 .0 .1

3.25

4.25

4.2

1.0

2.1

.2

.7

.1

.3

.1

.1

.0

.0

.0

.0

.0

.0

.0

.0

.0

4.75 — — ——— ——— —

NO. 3 SHOP

1361 BOARDS, BOARD MEASURE = 14,388 BOARD FEET

— — — — — Rerip pieces per 1000 board feet — — — — — 

1.75 27.9 15.8 4.6 2.5 .6 .2 .0 .0 .0

2.25 11.9 7.3 1.6 .6 .3 .1 .1 .0 .0

3.25 2.3 1.2 .1 .0 .1 .0 .0 .0 .0

4.25 .3 .2 .0 .0 .0 .0 .0 .0 .0

4.75 ———— — — — — —

Table 11.— Rip and rerip yield summary 

Shop Cutting yield Value

grade Rip Rerip Rerip Rip Rerip Rerip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

1 1 Ft2 Ft2 Pct Pct

No. 1 4,813 251 5.0 4,879 221 4.3

No. 2 14,346 411 2.8 13,775 334 2.4

No. 3 8,286 273 3.2 7,429 219 2.9

1

 Value has no units. 

11
References

1. Anonymous. 

1975. From tree to trim. Wood Moulding and 

Millwork Prod., 96 p., illus., Portland, Oreg. 

2. Anonymous. 

1978. Standard grading rules for western lumber. 

West. Wood Prod. Assoc., Portland, Oreg. March. 

3. Benson, Robert E. 

1971. Wood for millwork. USDA For. Serv. Res. Pap. 

INT-89, 10 p., illus., Ogden, Utah. 

4. Erickson, Bernard J., and Donald C. Markstrom. 

1972. Predicting softwood cutting yield by computer. USDA For. Serv. Res. Pap. RM-98, 15 p., 

Rocky Mt. For. and Range Exp. Stn., Fort Collins, 

Colo.

5. Petro, F. J., and F. M. Lamb. 

1968. Yield of clear cutting from lower grades of 

eastern white pine. Can. Dep. of For. and Rural 

Dev. Publ. No. 1227, 14 p., illus., Ottawa, Ont., Can. 

6. Stern, A. R., and K. A. McDonald. 

1978. Computer optimization of cutting yield from 

multiple-ripped boards. USDA For. Serv. Res. Pap. 

FPL 318, 12 p., For. Prod. Lab., Madison, Wis. 

7. Wise, Harold F. 

1964. Lumber recovery from old growth ponderosa 

pine and sugar pine on the Stanislaus National 

Forest-California. USDA For. Serv. Calif. Reg. 

26 p., illus., San Francisco, California. 

2.5-13-7/81

U.S. GOVERNMENT PRINTING OFFICE: 1981/75-027/8 

12