Determining the shape of the productivity function for mechanized felling and felling-processing

Productivity studies in forest operations are often carried out on new equipment, or on equipment being used in new conditions. Understanding how stand and terrain parameters impact the productivity of harvesting machines is important for determining their optimum use. Such information is normally presented as a productivity or efficiency function; that is, a regression equation that best represents the data. Most studies establish that piece size is the dominant predictor that impacts overall productivity. A common concept known as the “piece-size law” is that productivity increases at a decreasing rate with increasing piece size. What is not well understood is the upper limit to this piece-size law. That is, as the trees get “too” large, we can expect the machine to start to struggle, resulting in a decrease in productivity. Four different data sets—two based in New Zealand and two in Italy—are presented that clearly show an “optimum” piece size for maximum productivity. On average, productivity tended to decrease gradually, not drop off suddenly beyond the optimum. Using more complex statistical functions, it was possible to correctly correlate piece size to productivity.