Strandgard Martin, Mr.

There have been few comparative harvest system studies to provide a basis to understand the
performance and chip quality of harvest systems used in eucalypt plantations.
The study compared the CTL – cut-to-length method at the stump, WTM – whole tree method
where trees were processed to logs at roadside, IFC-DDC – infield chipping using a debark/
delimb/chipper, IFC-F/C – infield chipping using a separate flail and chipper harvest systems
on a single site in south-west Western Australia.
The WTM and IFC-F/C harvest systems were the most productive. The productivity of the
CTL and IFC-DDC harvest systems was about 25% less than that of the other harvest systems.
The CTL harvest system produced wood at the highest cost resulting from it having a large
number of machines without a correspondingly high productivity level. However, the CTL
harvest system has advantages over the other systems through retaining evenly distributed
logging residues, low machinery impact on the site and flexibility to add or subtract machines
as conditions change.
Two limitations of this study were that the harvest systems were only compared at a single
mean tree size and operator performance differences may have influenced harvest system
productivity. Previous studies have found that the balance of machines in a harvest system
can change with changes in mean tree size. This is an area where further research is required.
Wood chip samples from three of the four harvest systems did not meet the company chip
specifications. However, the deviations from the specifications were minor.

Drying forest biomass at roadside can reduce transport costs and greenhouse gas emissions by reducing its weight and increasing its net calorific value. Drying models are required for forest supply chain analysis to determine optimum storage times considering storage costs and returns. The study purpose was to evaluate the impact of the source of meteorological data on the goodness of fit and practical application of Eucalyptus nitens log pile drying models. The study was conducted in Long Reach, NE Tasmania, Australia from the 6th of February to 6th of August 2020. Four data sources were compared: the nearest meteorological station, interpolated meteorological data, a portable weather station, and digital temperature/RH sensors. Predicted moisture content (MC) values from the only previously published E. nitens log pile drying model were also evaluated using the current study data sources as inputs.

Log pile MC changes were determined from weight changes measured by placing the study logs on a steel frame bolted to load cells at each corner. As the study was based on debarked logs, dry matter losses were assumed to be negligible. Initial MC of the logs was determined by extracting samples using an electric drill and drying them until constant weight was achieved.

Initial log pile drying rates were high with several daily MC  losses >2%. Portable weather station data produced the best goodness of fit drying model. The second-best goodness of fit model was based on meteorological station data. From a user acceptability perspective (highest proportion of results within ±5% of measured values), the best model was based on temperature/RH sensor data. Goodness of fit measures for the temperature/RH sensor data model were poorer than for the other data sources, but still acceptable. The published E. nitens log drying model had the poorest results for goodness of fit and user acceptability.

In conclusion, portable weather stations are best suited to research trials due to the expense of placing a weather station at each log pile. Drying models based on data from the nearest meteorological station or temperature/RH sensors are best suited for practical applications, such as forest supply chain analysis. Additional benefits could accrue from a forest estate-wide network of low cost temperature/RH sensors potentially supplying data to forest supply chain analysis as well as fire prediction and tree growth models.