Improving wood quality assessment for Australia’s softwoods

Friday 6 Dec 2024

 
Forest & Wood Products Australia (FWPA) is pleased to share innovative research, which has significantly improved the accessibility and accuracy of wood quality information, providing softwood growers and processors across Australia with critical data to enhance their operations. The research utilises Resi technology for early selection of harvest-age density and modulus of elasticity (MoE), allowing for precise predictions of wood properties and yield.

The Resi technology, as discussed in the research, refers to a resistance drilling method used to assess wood properties, particularly in softwood plantations. It rapidly measures the resistance encountered when a drill bit penetrates the wood, which correlates with the wood’s density and stiffness of trees, logs and forest plots. The Resi tool is noted for its efficiency, being three times faster than other methods like the ST300 acoustic velocity measures, and it has been standardised for operational use across the industry to improve the accuracy and reliability of wood quality assessments.

This project has underpinned a transformation in the easy access to information about wood quality for softwood growers and processors. Resi data from pre-harvest assessments has accurately predicted mill site- mean board stiffness in sawing studies across multiple sites in Australia. It’s now a routine tool for many companies. The project is strongly aligned with the FWPA Strategic Plan and the focus on improving the resource base, as well as increasing productivity and utilisation along the value chain.

“I’m proud to have led a project that directly connects field-based log quality with the experiences of processors, delivering tangible benefits to the forest industry.” said  Associate Professor David Lee from the University of the Sunshine Coast.

The research showed that simulated mid-rotation Resi measurements can reliably estimate harvest age quality, aiding in informed decisions regarding rotation lengths. The study also addressed various sources of error between Resi instruments and techniques, establishing that these variances are negligible at a commercial scale, thereby fostering confidence in the consistency of Resi measurements.

“This research can help us enhance our operations and planning. With improved predictive modelling, we can make informed decisions about longer term resource planning and better understand timber production outputs.” said Rebecca Cherry, Wood Quality Engineer at Hyne Timber.

The key benefits for the industry include:
  • Successful predictions of mill site-mean board stiffness across multiple locations, has improved wood flow between growers and processors and has the potential to inform favourable log pricing structures.
  • Enhanced decision-making capabilities regarding rotation lengths, stocking, breeding objectives, and overall wood quality through predictive modelling.
  • Adoption of over thirty Resi instruments by growers and processors for routine inventory and log supply management.
  • Integration of wood quality predictions with yield estimates in YTGen software and enhancements to the Resi Processor software for better prediction of stiffness and density.
Key findings from the project include:
  • A confirmed relationship between pre-harvest measurements and mill production quality, demonstrating that Resi data can predict mill output at a compartment level.
  • Enhanced understanding of radial and longitudinal variation in wood quality, allowing for tailored silvicultural practices.
  • Development of improved algorithms incorporated into the new version of the Resi web trace processor, ensuring ongoing access to cutting-edge predictive models.
Looking ahead, the project recommends that Resi technology be further utilised to strengthen grower-processor communication, optimising wood flow and enhancing the value of plantations. A deeper understanding of how site, climate, management, and genetics affect MoE and structural grade percentages will be pursued using extensive, estate-wide datasets.

For more information and download the project report, please click here.

Source & image credit: FWPA


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