Sustainability Assessments and Management of Woody Waste

The utilization of forest harvest residues for renewable bioenergy production and bioproducts has increasingly become an integrated part of forestry that helps to meet the needs of climate change mitigation and a future carbon neutral economy. An essential element in the planning of any harvesting residue recovery operation is a reliable assessment of the quantity and quality of residue biomass and its composition over a harvest area. With the now widely adopted cut-to-length (CTL) at the stump harvesting system in Pinus radiata plantations in Australia, harvesting residues left on site are significantly larger in quantity and spatially more dispersed over a harvest area in comparison to the more traditional whole-tree harvesting. The conventional approach of assessing forest harvest residues through sample plots, transects, or small study blocks has provided site-specific estimates of residue biomass. However, these estimates cannot be readily extrapolated over the plantation landscape, which varies in silviculture, site, and stand conditions. To overcome this limitation, this study relied on harvester data analytics to obtain spatially explicit estimates of residue biomass using an example data set from harvested plantations of three stand types: unthinned (T0), thinned once (T1), and thinned twice (T2). Three methods of integrating biomass equations with harvester data were compared for residue biomass estimation: (1) applying individual tree biomass equations to harvested stems, (2) applying stand-level biomass equations to gridded harvester data, and (3) integrating estimates from the first approach with recorded and estimated waste volumes of harvested stems. The estimates of total residue biomass obtained using the three methods through harvester data analytics varied between 56.2 and 156.4 t/ha in green weight across the three stand types. These estimates were validated indirectly through ex situ sample plots and proved to be comparable to the quantities of residue biomass assessed using conventional sample plots, transects, or small blocks following CTL harvesting of rotation age P. radiata plantations elsewhere in Australia. Among the three methods, the third method made the most intensive use of the harvester data and provided the most realistic estimates of residue biomass. Spatial mapping of the estimated total and component residue biomass will assist the operational planning of residue recovery and site-specific nutrient management for the long-term sustainability of P. radiata plantations. Full article

Harnessing sustainably sourced forest biomass for renewable energy is well-established in some parts of the developed world. Forest-based bioenergy has the potential to offset carbon dioxide emissions from fossil fuels, thereby playing a role in climate change mitigation. Despite having an established commercial forestry industry, with large quantities of residue generated each year, there is limited use for forest biomass for renewable energy in Queensland, and Australia more broadly. The objective of this study was to identify the carbon dioxide mitigation potential of replacing fossil fuels with bioenergy generated from forest harvest residues harnessed from commercial plantations of Pinus species in southeast Queensland. An empirical-based full carbon accounting model (FullCAM) was used to simulate the accumulation of carbon in harvest residues. The results from the FullCAM modelling were further analysed to identify the energy substitution and greenhouse gas (GHG) emissions offsets of three bioenergy scenarios. The results of the analysis suggest that the greatest opportunity to avoid or offset emissions is achieved when combined heat and power using residue feedstocks replaces coal-fired electricity. The results of this study suggest that forest residue bioenergy is a viable alternative to traditional energy sources, offering substantive emission reductions, with the potential to contribute towards renewable energy and emission reduction targets in Queensland. The approach used in this case study will be valuable to other regions exploring bioenergy generation from forest or other biomass residues. Full article

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field. Full article

Many parameters can influence the weight of harvesting residues per hectare that remain on plantation sites after extracting sawlogs and pulpwoods. This study aimed at quantifying the impact of the cut-to-length and whole-tree harvesting methods on the weight of harvesting residues using 26 case studies in Australian plantations. A database was created using case studies conducted in different plantations, to measure the weight of harvesting residues per hectare and the components of harvesting residues. An analysis of variance was applied to test the impact made by the harvesting methods. The results confirmed that the cut-to-length harvesting method produced a larger weight of residues (104.0 tonnes of wet matter per hectare (tWM/ha) without additional biomass recovery and 64.7 tWM/ha with additional biomass recovery after sawlog/pulpwood extraction) than the whole-tree harvesting method (12.5 tWM/ha). The fraction test showed that stem wood formed the largest proportion of the harvesting residues in cut-to-length sites and needles were the largest component of the pine harvesting residues in sites cleared by the whole-tree harvesting method. The outcomes of this study could assist plantation managers to set proper strategies for harvesting residues management. Future research could study the impact of product type, silvicultural regime, stand quality, age, equipment, etc., on the weight of harvesting residues. Full article