Batch log studies are frequently used by sawmills to provide insight into lumber grade yields and overrun for a given log grade. This information is then used to determine log pricing. These batches often contain logs with a wide range of diameters and clear faces. Little research has been done to determine the reliability of a batch log study for use in determining log break-even pricing.

A series of 16 batch log studies were conducted at a hardwood sawmill to gain insight into the accuracy and reliability of the batch log study method. Batch compositions were found to be statistically different in four of five log grades. These statistically different batches led to statistical differences in lumber grade yields and overrun.

The batch log study method does not provide accurate insights into lumber grade yields and overrun. As a result, these data are not reliable for mill management decisions such as the calculation of log prices. Several changes could be made to improve the batch log study method, but the individual log study method would be of much more use to mill managers.

Log yield studies allow mills to better understand the products they manufacture from a given log, as well as the potential profit from those products and consequently, the purchased log. This accurate log yield data is vital for mill profitability during periods when lumber prices are weak or log supplies are tight, which, individually or in combination, lead to smaller profit margins. With accurate estimates of lumber yields by grade, overrun, sawing costs, and product pricing, mill management can predict break-even prices and set the maximum price to pay for purchased logs to ensure that raw material costs for the mill are reasonable and can sustain a desired level of profitability.

Results obtained from sawmill-based log yield studies, when combined with log information such as log diameter and scale, can be used to accurately value a log of a given species and grade. This eliminates the guesswork that typically occurs when mills are assigning prices to the logs they purchase. Without accurate log yield data, there is no way to price logs that will ensure their acquisition is profitable prior to being processed through the sawmill.

The importance and utility of log yield data have been recognized since the early development of hardwood log grading systems. Benson and Wollin (1938) suggested using lumber yield data as the basis of a future hardwood log grading system. This was the beginning phase of development for the US Forest Service (USFS) hardwood log grading system. Their work focused on defining the relationship between log defects and lumber grade. To achieve this, logs were scaled, and the defects were diagrammed. Logs were then tracked individually through the mill and lumber data were recorded for each log.

The USFS hardwood log grading system was completed in 1949 (Wollin and Vaughan 1949). This system was based on the individual log study approach, with data being collected from approximately 11,000 logs. This publication was later revised to update and adjust some of the original lumber yield data (Vaughan et al. 1966).

Many more log yield studies were completed using the individual log study method. Herrick (1946), in conjunction with the Purdue University Agricultural Experiment Station, worked to better understand lumber grade yields and overrun–underrun in Indiana hardwood sawlogs. Sawing time per log was also recorded to incorporate sawing costs. Calvert (1956) detailed a study by the Forest Products Laboratories of Canada to determine whether the USFS log grading system was useful in Canadian hardwood species.

Schroeder and Hanks (1967) present the results from an individual log study of 556 red oak (Quercus rubra) factory-grade logs. This work was continued to subfactory-grade red oak logs (Schroeder 1968). Hanks (1973) published results from subfactory-grade logs, with most commercial species included. The last significant lumber yield data from the Forest Service were published by Hanks et al. (1980). Additional yield data were combined with the previously published yield information presented in Vaughan et al. (1966).

After this time, no further effort was made by the USFS to continue collecting individual log data. However, the Appalachian Hardwood Center (AHC) at West Virginia University, in 2005 began collecting individual log data from hardwood sawmills and currently has data on over 4,600 logs in the AHC database. In conjunction with Appalachian Hardwood Manufacturers Inc., the AHC developed a standardized hardwood log grading system that was released in 2019. This system is a clear-face grading system similar to those in use at most hardwood sawmills today (AHMI 2019).

Hassler et al. (2019) discussed the reasons that the USFS hardwood log grading system was never adopted by the hardwood industry. This has led the industry to alternative methods of assessing the economics of their hardwood logs. Mills in general gravitated to mill-specific grading and scaling rules, rather than a standardized system. In order to understand their lumber grade yields and overrun in an effort to establish log pricing, mills have relied on batch mill studies. A batch mill study is a data collection process whereby a group of study logs are processed together, with data gathered for the group rather than producing any specific individual log data (Govett et al. 2006). Batch mill studies are somewhat simplified and less labor intensive than an individual log study, making the batch mill study easier for sawmills to conduct in an inexpensive manner without additional assistance. A batch is typically defined by species and log grade (Govett et al. 2006).

In contrast, an individual log study involves tracking individual logs through the sawing process. Each log in the study is numbered and each board produced from that log is labeled with the same number. This allows every board to be traced back to the log from which it was produced. Individual log studies are labor intensive, and can lead to reduced production, especially in larger sawmills.

The purpose of this article is to determine whether batch mill studies provide consistent lumber grade yield and overrun data, which are vital for the accurate pricing of logs. A series of break-even analyses were then conducted using the data gathered from the batch mill studies to determine the efficacy of the batch study system in developing log prices.

In order to conduct this study, it was necessary to engage a hardwood sawmill partner that had interest in improving log yield study accuracy. For this study, a hardwood sawmill in Pennsylvania was interested in cooperating on a project focused on determining the reliability of the batch mill study method. The annual production of the participating sawmill is >12 million board feet (MMBF). That mill traditionally conducted batch studies to collect log yields and expressed a keen interest in improving the batch mill study approach. Data were collected at the participating sawmill from August 2019 through March 2020.

The participating sawmill utilizes a clear-face grading system. Logs are graded based on the diameter inside the bark (DIB) at the small end of the log and the number of clear faces on the log. The grading system has five options for log grades. They are Prime, 1, 2, 3, and Cull. Figure 1 shows how the log grade changes across diameters and clear faces. For example, a 12-inch log with three or four clear faces is graded based on the log's position in the tree. A butt log would be a Grade 1, while an upper would be a Grade 2 log. Specific grade requirements for these options follow:

• Prime logs must have four clear faces and a scaling diameter of ≥16 inches.

• Grade 1 logs are subdivided into two categories: four clear faced or three clear-faced logs with a scaling diameter (DIB) ≥ 12 inches, but ≤ to 15 inches; or logs with 3 clear faces and ≥16 inches scaling diameter.

• Grade 2 logs are subdivided into two categories: two clear faces with a scaling diameter ≥ 12 inches, but ≤ 15 inches; or 2 clear faces with a scaling diameter (DIB) of ≥16 inches.

• Grade 3 logs are those with one clear face and any scaling diameter or any log (regardless of the number of clear faces) that has a diameter (DIB) < 12 inches.

• Cull logs are those logs with zero clear faces, regardless of diameter.

Figure 1.

Log grading specifications of the participating sawmill.

Figure 1.

Log grading specifications of the participating sawmill.

Close modal

It is important to note that the Figure 1 grading table is applied without recognition of species. That is, a four-clear-face log that is ≥17 inches, regardless of species, is graded as a Prime Grade log, and then placed in the log inventory by species. Species comes into play when selecting batches to study. For a Prime Grade red oak batch, Prime Grade logs would be selected at random from the inventory of red oak logs.

When logs arrive at the mill, they are scaled and graded by a two-person log inspection crew. Logs are graded as they lay, so at best, the inspectors can observe three faces of the log. No logs are rolled to enable the inspectors to view the bottom face. The logs typically are bunched very closely together and in most situations the inspectors are only able to observe one or two of the faces.

The participating sawmill collected all log scaling and grade data on a handheld computer. For each batch, a printout of batch data was provided. This printout provided the following information for each log: species, grade, length, scaling diameter, board foot volume, price per MBF, and price paid for the log. All logs were scaled using the Doyle log rule. A rule-of-thumb scaling deduction was used to account for log defects, where either log length or scaling diameter is reduced to account for the volume lost as a result of log defects (the exact rules-of-thumb were not disclosed). However, there was no indication in the printouts of when a scaling deduction was taken on a log, so only the revised scaling diameter or length was recorded. Additionally, the number of clear faces was not indicated on the tally sheets, so clear face information was assumed based on the assigned grade. For example, if the scalers classified a log as Grade 2, then only two clear faces should have been observed on the graded log. However, Grade 1 Small Diameter batches (≥12 in and ≤15 in) could contain either three or four clear faces in the grade. The number of clear faces on the logs was not recorded on the tally sheets, so there was no way to determine the number of clear faces for logs in these batches.

Logs in each batch were processed through the sawmill, with data collected for the batch as a whole. Each log was slabbed at the headsaw, producing a few boards during this process. All boards went to an optimizing edger. After primary breakdown at the headrig, flitches were sent to a gangsaw, where they were further processed into boards or small cants. Flitches are logs that have been sawn on two faces, with the other two faces still rounded as part of the log. Products were then cut to length at the trimmer and progressed to the lumber inspector, where National Hardwood Lumber Association (NHLA) grade, surface measure, and thickness were recorded. For each batch, lumber yield by grade and overrun were collected and analyzed.

Batch composition

A primary goal of this work was to determine the amount of variability between the batches in the study by comparing the frequencies in each cell of the grading table (Fig. 1) for batches of the same grade. For example, all Grade 1 small diameter batches, regardless of species, were analyzed to determine whether the batches were statistically different from each other. Comparing batches of the same log grade provided insight into how consistent the batch selection process was. Significance criterion for all tests was α = 0.05. Data were analyzed using JMP® Pro 14.0 (SAS Institute Inc., Cary, NC; Copyright 2015) and SAS® 9.4 (SAS Institute Inc.; Copyright ©2002–2012) software.

The Cochran–Mantel–Haenszel (CMH) test was selected over the Pearson chi-square test to identify significant differences between the composition of the batches (Stokes, 2012). Both tests use a chi-square distribution to test for significance, but the CMH test requires no expected cell frequencies in order to conduct the test. If a Pearson chi-square test had been used for this analysis, diameters would need to be grouped for the testing to ensure that at least 80 percent of the cells had an expected frequency of at least five. In contrast, the CMH test allows each diameter represented in the batch to be tested without any need to combine the diameters into groupings.

Lumber grade yields between batches were analyzed to determine whether batch composition had a statistically significant impact on lumber grade yields. This was done using the nonparametric Wilcoxon and Kruskal-Wallis tests. A Shapiro-Wilk W test was used to determine whether the distribution was normally distributed or nonparametric. Results from this test indicated that most distributions were nonparametric, which led to the use of the Wilcoxon and Kruskal-Wallis tests over a one-way ANOVA.

Both tests are nonparametric alternatives to the one-way analysis of variance (ANOVA). These two tests are similar to each other, with one major difference. The Wilcoxon test is used when there are two groups. The Kruskal-Wallis test is a nonparametric test like the Wilcoxon test, but it can accommodate more than two groups. Both the Wilcoxon and Kruskal-Wallis compare the test statistic with a chi-square distribution to determine statistical significance.

Certain species, specifically soft maple (Acer rubrum) and cherry (Prunus serotina) in this case, have color-based sorts (which could be considered a grade) of the higher quality lumber grades to meet market demand. These color-based lumber grades are unique to these species—no other species in this study were color sorted in this manner. To make similar lumber grades between species, lumber grades were classified into three broad categories. These categories were One Face and Better (1F+), 1 Common (1C), and finally, 2 Common and Below Plus Cants (2C - CANT).

The cants were combined with the two common and below grade lumber because different-sized cants were manufactured depending on species, and the size of the cant would have affected the yield percentage of both lumber and cants. For example, cherry cants were 3.5 inches by 6 inches, while for most other species, 5.5 inches by 6 inches cants were sawn.

If cant yield were tested in the analysis as a separate grade, the size of the cant would have skewed the yield percentage of both lumber and cants. Grouping cants with 2 Common and lower lumber minimized the effect of producing different size cants on grade yield percentages.

Overrun analysis

Overrun, usually expressed as a percentage, is the difference between the volume of lumber produced from a log and the estimated volume of the log obtained through scaling (Lin et al. 2011). Overrun was analyzed for each of the batch categories. Analysis of overrun was conducted in the same manner as lumber yields. The Shapiro-Wilk W test was used to determine whether the data were normally distributed or nonparametric. Results indicated that most distributions were nonparametric, leading to the use of the Wilcoxon and Kruskal-Wallace.

Break-even pricing analysis

The ultimate goal of any batch or individual log study is to collect data that can help establish log prices that most accurately and consistently reflect the ability of the mill to achieve a profit from the production of lumber products from those logs. The mill wants to make sure that as many logs as possible make a positive contribution to the mill's bottom line. For the purpose of analyzing log pricing, a break-even pricing analysis was conducted on the five Grade 1 Small Diameter batches. Break-even pricing avoids any reference to profit because that factor varies from mill to mill.

The grading system in Figure 1 does not distinguish between species, so the participating sawmill assumes that lumber grade yields and overrun, for each cell of the grading table, are the same regardless of species. Therefore, to illustrate the impact that variations in batch composition have on lumber grade yields and overrun, break-even prices were calculated for each batch using red oak lumber prices from the time of the study in 2019–2020, as provided by the participating sawmill. For soft maple and cherry, where color differentiations are made for first and second grade (FAS) or One Face and 1 Common lumber, the percentages were combined to provide a single FAS or One Face and a single 1 Common lumber price.

The goal of this analysis is not to determine whether lumber grade yields are different by species, but rather to illustrate the impact that batch composition has on break-even pricing. The break-even pricing analysis uses only red oak lumber prices and sawing costs because the underlying assumption of the participating sawmill's log grading system is that species has no impact on lumber grade yields or overrun. Simply put, for this break-even analysis, species plays no role in the analysis.

Table 6.

Break-even price analysis in US$per million board feet (MBF) for Grade 1 Small Diameter batches. Figure 2. Lumber grade yield comparison by batch. Figure 2. Lumber grade yield comparison by batch. Close modal Figure 3. Percent overrun by batch. Figure 3. Percent overrun by batch. Close modal Figure 4. Calculated break-even price by batch. Figure 4. Calculated break-even price by batch. Close modal Accurate and consistent pricing of sawlogs is vital to ensure a profitable sawmill operation, and when a mill is utilizing batch studies for that purpose there are two fundamental factors to consider. First, is the structure of the batches, over the range of log grades, sufficient for providing accurate grade yield, overrun, and pricing results? In the case presented here, is the breakdown of grades for a batch, as detailed in Figure 1, going to provide a sufficient level of accuracy and consistency? Second, given the structure of the batch protocol, as reflected in Figure 1, are the batches configured to produce the most consistent results? In other words, are the samples within a batch skewed to one diameter and clear face combination, as opposed to a uniform set of sample logs across the diameter and clear face combinations for that grade? The batch structure for the mill was already set as illustrated in Figure 1; therefore, then the actual composition of the batches effectively defined the efficacy of the structure. In effect, the batch composition defines the expected lumber grade yields and overrun and whether they in turn provide sufficient consistency and accuracy in pricing sawlogs. Variation in batch composition The Cochran–Mantel–Haenszel analysis tested to determine whether log diameter frequencies were different between batches of the same grade designation (e.g., Prime Grade batches, of which there were three). Table 7 summarizes the results. It is clear that all of the batch compositions, except for Grade 1 Large Diameter, were significantly different, suggesting that accuracy and consistency may be compromised. The question then is whether statistically differing batch compositions lead to statistically different lumber grade yields and overrun. Table 7. Summary of batch composition results. Table 8 summarizes the lumber grade yield results. Seven of 15 lumber grade yields were statistically different, while 8 instances were not. In the latter eight instances, seven of those were from batch types that include only two diameter classes. In all but one case, where four diameter classes were included in the batch, the lumber grade yields were statistically different. The one exception was for the Grade 2 Small Diameter, One Common batch, which had a nearly significant P value of 0.0734. Table 8. Summary of lumber grade yield results. The implication of these analyses is that the fewer combinations of diameter and clear faces in a batch, the more likely that the variation will be smaller. Similarly, three of the batch grade designations (Table 9) showed overrun to be statistically significant, with one of those showing a pairwise difference, without an overall significant result. Although the statistical differences were not uniformly significant, the actual differences were a concern for all batches except the Prime Grade batches, ranging from 15.5 to 22.0 percent. The magnitude of these differences is a concern because such wide swings in overrun directly affect log pricing, as illustrated in the wide swings in price for a given batch type. Table 9. Summary of batch overrun results. Potential financial impact of improper batch results Statistically significant differences in batch composition led to statistically significant differences in both lumber grade yields and overrun in many batches. This in turn produced relatively large differences in batch break-even pricing. The lowest batch break-even price was US$449.05 per MBF, while the highest break-even price result was US$492.79. The difference between the highest and lowest batch break-even prices was US$43.74 per MBF or a 9.7 percent increase from lowest to highest.

In the highly competitive hardwood market with its small profit margins, accurate and reliable log pricing information is critical. When the difference of US\$43.74 per MBF is compounded through a year of sawmill production, it amounts to a large sum of money. A small difference in log purchase price could very easily be the difference between a profitable or unprofitable operation.

Drawbacks of the batch study method and suggestions for improvement

The batch mill study approach has several problems related to the collected data when trying to accurately price logs. In combination, these factors work to limit the reliability and accuracy of the batch mill study approach as it relates to determining break-even pricing of logs. Problems with the batch study approach include the following:

• The lack of log-specific data: In the batch study approach, there is no way to track lumber yields by log. At the end of the batch study, the main results are lumber yields by grade and overrun. These data are gathered for the entire batch, not for each log, which is a major issue when batches have a wide range of diameters and clear faces. Small variations may exist in sawing patterns that will undoubtedly lead to changes in lumber grade yields. This is a potential problem in both the batch and individual log study methods. If a mill is serious about gathering log yield data, these small variations in sawing pattern would have minimal effect with a sufficiently large data set.

• Limited statistical options: The batch study approach provides only one result at the conclusion of the analysis. As such, a batch study is essentially one observation. Even though the batches contained 20 or 25 logs each, the results provide one observation into lumber grade yield percentages and overrun. No statistical information, such as mean, standard deviation and confidence intervals, can be computed for a single observation.

• Break-even price is heavily influenced by log diameter frequencies: Each batch generally contains a range of diameters rather than one single diameter, so the break-even price is weighted toward the log diameter occurring most frequently in the developed batch. This is of particular concern because overrun increases with decreasing diameter when using the Doyle log rule, so that a heavy proportion of smaller diameter logs will increase overrun from that batch. This has the net effect of skewing the break-even price and minimizes the pricing impact of log diameters that were less frequent in the batch.

• Large amounts of variability within batches of the same log grade: When incorrectly graded logs are included in a batch, even though they should not be in that batch, break-even price will be adversely affected by the presence of those logs. An example of this is in the Red Oak Grade 2 Large Diameter batch. A majority of logs in this batch were incorrectly included in the study. This batch undoubtedly led to unreliable estimates of lumber grade yields and overrun for this log grade, which in turn affects the accuracy of break-even price estimates.

If a mill is constrained to conducting only batch studies, there are several ways to improve the batch study approach to improve accuracy and reliability.

• Each batch should be composed of logs of the same grade, with a very narrow diameter range. An ideal batch is one that is focused in one cell of the grading table. For example, a well-defined batch would be a 12-inch, four-clear-face batch. A batch with a wide range of diameters or clear faces does not produce reliable, accurate results.

• Ensure logs to be included in a batch study are correctly scaled and graded. Ideally, logs should be rolled so that all four faces of every log can be observed.

• More than one batch study should be conducted for each cell in the grading table. This will allow statistics, specifically the means and standard deviations to be calculated, further verifying the reliability of the batch study results. However, conducting enough batch studies to develop an adequate number of observations may be too expensive for a mill to undertake. The alternative is to take the time to collect individual log data so that each study contributes multiple observations to the mill's individual log data set.

• Ensure that the mill and head sawyer are consistent in the way each log is sawn. This allows the mill to avoid suboptimal yields from individual logs and improve the ability of both batch and individual log studies to accurately estimate log yield.

Based on this study, the batch mill study approach is not a reliable way to set log prices, especially using the methods detailed here. With some of the recommended improvements in data collection, the batch mill study has the potential to improve the reliability and accuracy of break-even log pricing estimates but further study is needed to confirm this.

A major issue with the batch study, as observed in this study, is that logs were not rolled as part of the log inspection process. When using a clear-face grading system, it is absolutely critical that all four faces of the log be observed by the log inspectors.

Further study is needed to determine if the batch study method can provide more accurate and reliable log pricing results when the batch is composed of logs of a single log grade and diameter. Based on the results from this study, batch study data can potentially lead to log purchases at costs well above their actual break-even value. Hardwood sawmills would be better served to use individual log studies, even though these studies are more time consuming, to improve mill profitability.

Part Two of this study will compare the batch results presented here with an individual log study conducted on these same logs. In the individual log study, logs were rolled as part of the log inspection process. Lumber grade yield and overrun data were collected for every log, which allows for a direct comparison of the results from the two mill study methods.

This work was supported by a grant from the USDA Forest Service Wood Innovations Program [Grant Number 18-DG-11420004-288].

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