Harvey Campbell, PhD

Background: Small-scale forests (woodlots) increasingly account for a greater proportion of the total annual harvest in New Zealand. There is limited information on the extent of infrastructure required to harvest a woodlot; road density (trafficable with log trucks), landing size, or the average harvest area that each landing typically services.

Methods: This study quantified woodlot infrastructure averages and evaluated influencing factors. Using publicly available aerial imagery, roads and landings were mapped for a sample of 96 woodlots distributed across the country. Factors such as total harvest area, average terrain slope, length/width ratio, boundary complexity and extraction method were recorded and investigated for correlations.

Results: The average road density was 25 m/ha, landing size was 3000 m2 and each landing was serviced on average 12.8 ha. Notably, 15 of the 96 woodlots had no internal infrastructure, with the harvest completed using roads and landings located outside of the woodlot boundary. Factors influencing road density were woodlot length/width ratio, average terrain slope and boundary complexity. Landing size was influenced by average terrain slope, woodlot length/width ratio, and woodlot area.

Conclusion: The results provide a contemporary benchmark of the current infrastructure requirements when harvesting a small-scale forests in New Zealand. These may be used at a high level to infer the total annual infrastructure investment in New Zealand’s woodlot estate and also project infrastructure requirements over the foreseeable future.

The aim of the study was to provide a comprehensive overview of global long-distance road transportation of industrial roundwood. The study focused on the maximum gross vehicle weight (GVW) limits allowed with different timber truck configurations, typical payloads in timber trucking, the road transportation share of the total industrial roundwood long-distance transportation volume, and the average long-distance transportation distances and costs of industrial roundwood. The study was carried out as a questionnaire survey. The questionnaire was sent to timber transportation logistics experts and research scientists in the 30 countries with the largest industrial roundwood removals in Europe, as well as selected major forestry countries in the world (Argentina, Australia, Brazil, Canada, Chile, China, Japan, New Zealand, South Africa, Türkiye, the United States of America and Uruguay) in February 2022, and closed in May 2022. A total of 31 countries took part in the survey. The survey illustrated that timber trucking was the main long-distance transportation method of industrial roundwood in almost every country surveyed. Road transportation averaged 89% of the total industrial roundwood long-distance transportation volume. Timber truck configurations of 4 to 9 axles with GVW limits of around 30 tonnes to over 70 tonnes were most commonly used. The results indicated that higher GVW limits allowed significantly higher payloads in timber trucking, with the lowest payloads at less than 25 tonnes, and the highest payloads more than 45 tonnes. The average road transportation distance with industrial roundwood was 128 km, and the average long-distance transportation cost in timber trucking was €11.1 per tonne of timber transported. In the entire survey material, there was a direct relationship between transportation distance and transportation costs and an inverse relationship between maximum GVW limits and transportation costs. Consequently, in order to reduce transportation costs, it is essential to maximise payloads (within legal limits) and minimise haul distances. Several measures to increase cost- and energy-efficiency, and to reduce greenhouse gas emissions in road transportation logistics, are discussed in the paper. On the basis of the survey, it is recommended that up-to-date statistical data and novel research studies on the long-distance transportation of industrial roundwood be conducted in some countries in the future.

Modern forest harvesting machinery generate an abundance of underutilised data in their control systems. The Controller Area Network (CAN) bus data stream offers the opportunity to investigate the operation of the machinery in detail while in real-world harvesting scenarios. This study uses CAN data to assess a component of operator workload in forwarder operations, by introducing a method to interpret forwarder joystick movements. The data was captured in a clearfell logging operation case study in Canterbury, New Zealand. The joystick data was then analysed to determine the time and number of operator input movements per load cycle (»grab«) totalling 418 grabs. This, combined with video analysis, identified independent variables that describe the grab cycle such as time of day, number of logs per grab, log grade, and the activities of »pencilling« (vertical drop of logs in grab to align large ends) and »dropping« (releasing logs from the grab before loading). Factors that significantly affected the operators' time taken to complete the grab and the number of required joystick movements included number of logs, pencilling and dropping. For example, the average load cycle was 18-seconds for four logs, and this increased by 6.1-seconds and 14.4-seconds per grab when pencilling or dropping, respectively. Average total joystick movements were ~108 per grab. This case study demonstrated that CAN bus data can be used to improve our understanding of the operation of harvesting equipment such as forwarders. An example use of the result is to share and compare this with the harvester operator (who crosscuts and sets out the logs for forwarder-collection) as the need for pencilling and dropping is a consequence of misalignment or debris caught in the piles. The method used also presents an opportunity for human factors research, particularly in operator fatigue management and training through the measurement of joystick movements with a genuine possibility of real-time performance feedback.

Modern forest harvesting machinery generate an abundance of underutilised data in their control systems. The Controller Area Network (CAN) bus data stream offers the opportunity to investigate the operation of the machinery in detail while in real-world harvesting scenarios. This study uses CAN data to assess a component of operator workload in forwarder operations, by introducing a method to interpret forwarder joystick movements. The data was captured in a clearfell logging operation case study in Canterbury, New Zealand. The joystick data was then analysed to determine the time and number of operator input movements per load cycle (»grab«) totalling 418 grabs. This, combined with video analysis, identified independent variables that describe the grab cycle such as time of day, number of logs per grab, log grade, and the activities of »pencilling« (vertical drop of logs in grab to align large ends) and »dropping« (releasing logs from the grab before loading). Factors that significantly affected the operators' time taken to complete the grab and the number of required joystick movements included number of logs, pencilling and dropping. For example, the average load cycle was 18-seconds for four logs, and this increased by 6.1-seconds and 14.4-seconds per grab when pencilling or dropping, respectively. Average total joystick movements were ~108 per grab. This case study demonstrated that CAN bus data can be used to improve our understanding of the operation of harvesting equipment such as forwarders. An example use of the result is to share and compare this with the harvester operator (who crosscuts and sets out the logs for forwarder-collection) as the need for pencilling and dropping is a consequence of misalignment or debris caught in the piles. The method used also presents an opportunity for human factors research, particularly in operator fatigue management and training through the measurement of joystick movements with a genuine possibility of real-time performance feedback.