Ackerman Simon A., MSc.

There has been a concerted shift from traditional motor-manual and semi-mechanised timber
harvesting systems to mechanised cut-to length (CTL) operations in South Africa. This is
particularly true in Eucalyptus pulpwood felling and processing, South Africa’s largest commercial
wood resources used in the pulp and paper industry. Mechanisation improvements
are typically driven by increasing safety regulations, product quality and productivity concerns
related to traditional harvesting systems. The objective of this study is to develop productivity
models for mechanised Eucalyptus pulpwood CTL felling and processing operations by combining
the results of a number of individual studies done over a period of 24 months in the summer
rainfall areas of South Africa. The study takes into account species, machine type (purpose
built vs. excavator based), silvicultural practices (planted vs. coppiced) and slope. The pooled
data revealed general productivity ranges from 5.16 m3 PMH-1 to 27.49 m3 PMH-1.

Accurate predictions in forest operations can be used towards effective planning, costing, and maximizing the productivity of machines in mechanised cut-to-length (CTL) harvesting. There is a general and substantial gap in forwarder productivity data available for pine sawtimber in South Africa at present, and as the number of product assortments being harvested increase there is a need for more work to quantify the effects of extracting products of different dimensions. The aim of this study was to calculate the time consumption and productivity of two models of Ponsse forwarders (15 t and 20 t capacity) to consider and compare the effects of multiple variables including machine capabilities, product assortment, load size, extraction distance and fuel consumption. Productivity averaged at 34.08 m3 per productive machine hour excluding delays longer than one minute (PMH1) for the smaller machine, and 55.94 m3/PMH1 for the larger machine. Productivity and average log volume were strongly positively correlated. Regression models were created for each machine where load volume and extraction distance were both significant factors for predicting productivity. Average fuel consumption of the smaller machine was 15.55 l/PMH1 and 0.47 l/m3, and 20.57 l/PMH1 and 0.43 l/m3 for the larger machine. The product with the largest volume was found to require the least fuel per m3. The models developed could aid in predicting system productivity and potentially carbon emissions under similar conditions in a South African context of industrial plantation forestry.