Development of Conversion Factor from Volumetric to Weight Measurement of Acacia crassicarpa and Eucalyptus sp. Pulpwood

2.1 Time and research location

The study was carried out between January 2023 and November 2024. The research phases encompassed the coordination of research methods, identification of data availability, and development of data collection techniques. Data collection occurred between August and October 2024 at PT Riau Andalan Pulp and Paper and PT Arara Abadi in Riau Province. The data analysis occurred between October and November 2024.

2.2 Instruments and materials

The instruments employed for data collection include wood diameter measuring tools (diameter tape/phi band), length measuring tools/meter gauges, digital scales with a 100 kg capacity, SM measuring tools (1 m × 1 m × the length of the wood), the global positioning system (GPS), stationery, and tally sheets. The materials utilized include wood logs, wood inventory data, PBPH documents related to the management of wood products, the Certificate of Legality of Wood Forest Products (Surat Keterangan Sahnya Hasil Hutan Kayu (SKSHHK)), and weighing documents from the factory or mill.

2.3 Methods

Research typically proceeds through stages of data collection and analysis, encompassing the following steps:

1. Collecting felling data. The felling data encompasses the location and area of the harvesting site, harvest time, quantity of logs, log diameter, and felling volume.

2. Gathering data on the wood stack. The wood stack data pertains to the total SM weight derived from a single unit of harvesting documentation and the corresponding weight of wood within the industry.

3. Examination of the transfiguration of the SM volume to weight units.

4. Analyzing variations in wood weight attributable to harvesting time intervals, transport distances, and alterations in transportation modes, utilizing the felling documentation as the analytical unit.

5. Examining variations in wood weight during harvesting in comparison to the weight of wood utilized in the industry.

6. Developing a conversion table that incorporates wood type, transport distance, and mode of transportation.

2.4 Data collection

The data collection methods employed include a wood logs approach, subsequently referred to as the single-tree part method, and a document approach, later designated as the full-tree method. The single-tree part method entails measuring the length, diameter, and weight of the wood logs as a unit. The full tree method entails collecting data from SM and SKSHHK documents. The SKSHHK document is an official certification issued by authorized officials to validate the legality of the transportation, control, and ownership of forest products. This document verifies that the forest products in question are sourced from a legitimate origin in compliance with statutory regulations. The SKSHHK facilitates the transportation of forest products from state forest areas, superseding earlier documents like the Round Wood Transportation Letter (Surat Angkutan Kayu Bulat (SAKB)) and the Processed Wood Transportation Letter (Surat Angkutan Kayu Olahan (SAKO)). The issuance of the SKSHHK is intended to guarantee the legality of traded or transported forest products, thereby promoting sustainable forest management and mitigating the circulation of illegal forest products. The SKSHHK also functions as the foundation for the collection of levies or taxes associated with specific forest products. The data collection is categorized into two classes based on log length: one measuring 4 meters and the other measuring 6 meters, with the trunk being either barked or debarked under the conditions specified.

This method provides data on the length, base diameter, tip diameter, and weight of wood logs, measured in SM. The measurement of the center diameter was conducted on wood logs measuring 6 meters in length. The data was collected at the Timber Shelter (Tempat Penimbunan Kayu (TPK)) and the log yard, which serve as the wood storage areas within the mill. Information regarding the duration of wood storage in the log yard and the condition of the barked or debarked trunks underpins the data at the log yard. Additionally, secondary data comprises SKSHHK documents and wood weighing records. The two datasets are primary in the analysis and are interrelated.

2.4.1 Data collection at the Tempat Penimbunan Kayu

Data collection at the TPK was performed via sampling methods. The sampling method was random and contingent upon the availability of logs. The stages of data collection at the TPK were as follows:

1. Construct an SM measuring tool with dimensions of 1 meter wide and 1 meter high. The length of the measuring tool corresponded to the length of the logs according to the PBPH wood management protocols.

2. Place the logs inside the SM measuring tool until it reaches a width of 1 m and a height of 1 m.

3. Count the quantity of logs in the SM measuring tool.

4. Measure the diameter of each log, including the base and tip diameters, in centimeters.

5. Measure the length of each log, in centimeters.

6. Weigh each log in kilograms.

7. Record measurement results on a tally sheet.

8. Measurements were performed a minimum of three times, considering criteria such as wood type, trunk condition (barked or debarked), wood stockpiling duration, and log length.

Alongside the application of the SM measuring tool, measurements of logs were performed on logging trucks carrying timber. The subsequent procedures were employed to quantify the logs on logging trucks:

1. Measure the dimensions of the logging truck bed, including length, width, and height. The measurements were subsequently converted to SM. The truck used in this study consisted of two stacks. The stacks measured 8 m in length and 2.4 m in width, with the height adjusted according to the volume of timber being transported. Each stack thus had dimensions of 4 m × 2.4 m × the height of the wood stack on the truck.

2. Measure the diameter of each log in the logging truck bed, including the base and tip diameters, in centimeters.

3. Measure the length of each log, in centimeters.

4. Weigh each log in kilograms.

5. Count the quantity of logs in the logging truck bed.

6. Record the measurement results in a tally sheet.

7. Measurements were performed a minimum of three times, considering criteria such as wood type, trunk condition (barked or debarked), duration of wood storage, and log length.

All measurement results were documented in a tally sheet. Table 1 presents the tally sheet for the measurements of wood logs conducted at the TPK.

Table 1. Example of a tally sheet for measuring logs at Tempat Penimbunan Kayu

Note: L: log length (m); D1: the base diameter (cm); D2: the tip diameter (cm); W: log green weight (kg)

In the felling block, each log is measured and subsequently loaded onto a transport truck in accordance with the SM size designed by the PBPH. Transport vehicles exhibit varying dimensions, leading to discrepancies in meter stack sizes. Measurements at the felling block vary based on tree species and bark condition, specifically whether it is barked or debarked. Figure 1 illustrates the data collection conducted at the felling block.

Figure 1. Data collection at the felling block (TPK)

The A. crassicarpa species generally has a more curved trunk shape than the Eucalyptus sp. species. Harvesting in the felling block produces logs in two lengths: 4 m and 6 m, and also in two trunk conditions: barked or debarked. In field practice, only Eucalyptus sp. species receive the bark peeling treatment, which is adjusted to meet industry demand. Table 2 presents the number of sample wood stacks collected and categorized by species, log length, and trunk condition (barked or debarked). In contrast, Table 3 provides the total number of individual logs corresponding to these sampling categories, allowing for more precise volume estimation and operational analysis.

Table 2. Number of sample wood stacks

Table 3. Number of sample logs

2.4.2 Data collection at the log yard

Data collection at the log yard was performed using purposive sampling methods. Sampling at the log yard was performed purposefully. The parameters considered were wood type, storage duration, trunk conditions (debarked or barked), and log length. The parameters were subsequently designed as treatments. Each treatment was replicated at least three times. The duration of wood storage was modified according to data provided by field officers and log entry/exit registration records at the log yard. It is anticipated that data will be available for newly arrived wood from the TPK (fresh cut), wood stacked for less than one week, and wood stacked for more than one week.

The stages of data collection at the log yard are outlined as follows:

1. Construct an SM measuring tool with dimensions of 1 meter wide and 1 meter high. The length of the measuring tool corresponded to the length of the logs according to the PBPH wood management protocols.

2. Place the logs inside the SM measuring tool until it reaches a width of 1 m and a height of 1 m.

3. Count the quantity of logs in the SM measuring tool.

4. Measure the diameter of each log, including the base and tip diameters, in centimeters.

5. Measure the length of each log, in centimeters.

6. Weigh each log in kilograms and grams.

7. Record measurement results on a tally sheet.

8. Measurements were performed a minimum of three times, considering factors such as wood type, trunk condition (barked or debarked), duration of wood storage, and log length.

All measurement results are documented in a tally sheet. Table 4 presents the format of the tally sheet utilized for measuring logs in the log yard.

The measurement of each log occurred at the log yard, where logs were measured and loaded into an SM measuring tool with dimensions of 1 m × 1 m × the length of the logs (4 m and 6 m), as illustrated in Figure 2.

Field measurements exhibit variations based on tree species, trunk condition (barked or debarked), and duration of exposure in the log yard. Figure 3 illustrates the data collection process at the log yard.

Table 4. Format of the tally sheet utilized for assessing wood logs in the log yard

Note: L: length of logs (m); D1: the base diameter (cm); D2: the tip diameter (cm); W: log green weight (kg); S: trunk condition (barked/debarked)

(a) SM measuring tool

(b) Logs loading process into the tool

Figure 2. The logs measurement using the SM measuring tool at the log yard

Table 5. Logs sample data at the log yard

This variation was quantified using a log scale. Three parameters were analyzed: wood type, trunk condition (barked or debarked), and the duration between felling and storage in the log yard. The time interval was categorized into three groups: freshly received logs from the log yard (TPK), referred to as fresh cut; logs aged up to one week; and logs aged up to two weeks. Table 5 presents a summary of the data.

(a) The measurement of logs weight

(b) The measurement of the diameter of the logs

Figure 3. Data collection for the logs' weight and diameter at the log yard

2.4.3 Data collection for Surat Keterangan Sahnya Hasil Hutan Kayu documents and wood weighing documents

The collected documents encompassed data regarding the management of PBPH timber products, specifically the SKSHHK (Sub-Property Certificate) and weighing documents from the mill. The SKSHHK document served as a transportation record for timber forest products, encompassing essential data such as the SKSHHK number, details of the sender and recipient, transportation method, origin location, timber type, and volume measured in SM and m³. The weighing document at the mill included comprehensive data concerning the SKSHHK number, transportation method, arrival date, and time of the timber, as well as the weight of both the transport equipment and the wood itself.

The SKSHHK document was established using data gathered sequentially over at least three years, or through a representative snapshot of each month within that three-year time frame. Five documents were issued monthly. The objective of data collection from these documents was to acquire pertinent data and information, thereby illustrating the correlation between timber volume in SM and m³ from the upstream industry, the weight of timber upon arrival at the mill, and the length of timber transportation.

Data collection using the whole tree method was conducted, employing an SM volume and weight calculation approach at the transportation unit scale, in accordance with the SKSHHK document. The SKSHHK documents catalogued encompass records from the past three years, accounting for seasonal variations, specifically the dry and rainy seasons, to reflect annual conditions. Data derived from the SKSHHK document is summarized in Table 6.

Table 6. Number of SKSHHK data (documents)

2.5 Data analysis

The analysis was conducted on the collected data, encompassing both field measurements and documentary sources.

2.5.1 Data analysis of field measurements

Calculation of the volume was performed for each log. The volume of each log was calculated using field inventory data, which included the base diameter, tip diameter, and length of each log. The calculation of the volume of logs was conducted utilizing the Brereton formula method (Eq. (1)), as shown below:

$V=0.25 \pi\left(\frac{(d p+d u)}{2}\right)^2 \times L$              (1)

where,

V: volume (cm³)

$\pi$: 22/7 or 3.14

dp: base diameter (cm)

du: top diameter (cm)

L: length (cm)

The Brereton formula provides a robust estimate of solid wood volume under bark, accounting for log taper by using the mean of base and tip diameters.

To convert the volume of logs from cm³ to m³, apply Eq. (2) as follows:

$V S=\frac{V}{1,000,000}$             (2)

where,

Vs: volume in cubic meters (m³)

V: volume in cubic centimeter (cm³)

Calculation of the ratio between the volume of the log and the weight of each log was carried out. This ratio was derived by comparing the volume of the log to the weight of each log. This ratio facilitated the transformation of tonnage to cubic meters. This proportion is expressed as Eq. (3), below:

Proportion $\left (\frac{m^3}{ton}\right)=\frac{log \, volume \,  \left(m^3\right)}{log \,  green \,  weight \,  (ton)}$               (3)

Calculation of log volume in cubic meters was performed. The log volume in one standard meter (k) is the aggregate of all logs that occupy one standard meter or SM volume, expressed as follows (Eq. (4)):

$S M=\sum_{k=1}^n \mathrm{Vk}$          (4)

where,

SM: Stacked cubic meter (m³ (st))

Vk: volume of the k-th wood cut (m³)

n: number of logs in a stack

Calculation of the conversion factor from SM to solid cubic meters was performed. The conversion factor from SM to solid cubic meters was determined by comparing the volume of the logs constituting one SM with the volume of the SM itself. The volume of SM was calculated based on the length of the logs; specifically, a log with a length of 4 m corresponds to a SM volume of 4 m³, while a log with a length of 6 m results in a SM volume of 6 m³. The log conversion factor was formulated based on the SM volume as follows (Eq. (5)):

$C F=\frac{\sum_{k=1}^n(V k)}{S M}$          (5)

where,

CF: conversion factor

SM: stacked cubic meter (m³ (st))

Vk: logs volume

2.5.2 Data analysis of the log documents

Calculation of the ratio between the log volume specified in the document and the net wood weight obtained from the mill for each log category was carried out. This ratio was derived by analyzing the total volume of logs in relation to the total weight of logs in each document. This ratio was utilized to convert tonnage into cubic meters of logs. This proportion is expressed as follows (Eq. (6)):

Proportion by doc $\left (\frac{m^3}{ton}\right)=\frac{Total \, volume \,  in \,  a \,  doc \left(m^3\right)}{ Wet \, weight \,  in \,  a \,  doc \, (ton)}$           (6)

Conversion factors of logs from volume (m³) to weight (tons) were performed. These factors were derived from the subsequent analytical steps:

1. Data were gathered from monthly and annual summaries within the Company's Wood Administration Department.

2. The chosen data encompassed various seasonal conditions, including dry and rainy seasons, resulting in a considerable range of differences associated with transportation seasons.

3. The chosen data facilitated the calculation of log conversion factors by comparing the total log volume in an individual document with the total log weight in that same document.

2.5.3 Calculation of conversion factor

The confidence interval for each conversion factor at the 95%, 90%, and 80% level was calculated using the formula (Eq. (7)):

$\bar{x} \pm t_{\alpha / 2, n-1} . S E$                      (7)

where,

$\bar{x}$: the average of the conversion factor

t: t-table factor

SE: standard errors

2.5.4 Factors influencing the conversion rate from cubic meters to tons

Conversion factors were established to facilitate the equitable and objective measurement of potential wood yield, employing various units of measurement, including volume and weight. These factors enhanced the effectiveness of wood management processes, including:

1. Production planning. Companies could enhance the accuracy of pulp or paper production planning by assessing the available wood volume in the forest.

2. Evaluation of efficiency conversion factors served as a metric for assessing the efficiency of the wood processing process, encompassing activities from felling to final production.

3. Cost calculation conversion factors played a crucial role in determining production costs, as they affect the quantity of wood needed for the manufacture of a specific product.

4. Sustainable forest management. Understanding conversion factors enabled companies to manage plantations sustainably, ensuring that wood utilization remains within the forest's regeneration capacity.

Several factors, including tree species, age, health, and processing methods, influenced conversion factors. Additional factors encompassed the diverse biophysical and topographical characteristics of forests, the precision of wood volume assessments, and developments in wood processing technology.

Analysis of factors influencing the conversion factor magnitude from SM to m³ or tonnage using RStudio R 4.3.1 with the Agricolae packages was performed. ANOVA was used to analyze normality by wood species, log length, and trunk conditions. Further, the Agricolae package was utilized for the Duncan Multiple Range Test (DMRT) with a significance level of 5%.

The calculation of the error rate if one of the variables, such as type (sp), log condition (tc), stacking time (t), and log length (l), is not included in estimating the conversion of weight to cubic meters. We performed bootstrapping on the regression. Bootstrapping simulations were performed on sp, tc, t, and l. The error rate was calculated using the formula (Eq. (8)):

$e_i=\sum \frac{\mu_i-\mu}{\mu} \times 100$             (8)

where,

$e_i$: Error factor if not considering the independent variable i;

$\mu_i$: The mean factor of the weight estimation model if the independent variable i is not considered in the model;

$\mu$: The mean factor of the complete regression model.

The complete regression model is (Eq. (9)):

$w=\alpha+s p+t c+t+l+\varepsilon$          (9)

The research findings indicated that conversion factors were used to convert SM to solid wood volume, which were currently used for estimating harvest yields. Conversion factors required examination of the species, log length, and trunk condition, particularly whether it was barked or debarked. While converting solid cubic meters to weight required consideration of tree species, the condition of the barked or debarked, and the time elapsed from felling to processing. Ignoring these factors could result in potential conversion errors of up to 39.56%.