Productivity of a Ponsse Ergo Harvester Working on Steep Terrain

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1 Productivity of a Ponsse Ergo Harvester Working on Steep Terrain M. Chad Bolding and Dr. Bobby L. Lanford Graduate Research Assistant and Associate Professor Emeritus School of Forestry and Wildlife Sciences M. White Smith Hall, Auburn University, AL ABSTRACT The objective of this study was to analyze the productivity of a Ponsse Ergo harvester using a H73 head working in a mixed pine and hardwood forest on steep slopes in north Georgia. Productivity data were collected on the harvester from five one square chain plots. Each plot represented a different stand and slope condition. Slopes ranged from 0 to 46 percent. The harvester was videotaped performing the operations of felling, limbing, and bucking. Time and tree data were extracted for an elemental time study. Estimated quantities were defined in units of time per tree and included move time, swing time, fell time, process time, and total productive time. The possible predictor variables were identified as DBH, total height, number of bucks, tons per acre, trees per acre, slope, and species. Slope was found to be a significant variable for predicting move time, swing time, and total productive time. The variables of DBH 2, total height, slope, and trees per acre with a square-root inverse transformation were found to be significant for predicting total productive time per tree when total height was known. Number of bucks, tons per acre, and species were found to be non-significant variables for predicting any of the dependent variables. It was not surprising that slope had an effect on the productivity of the harvester. Interestingly though, the analysis found species variation to be non-significant for predicting any of the analyzed dependent variables. That is to say that even on steep slopes, the Ponsse Ergo harvester harvested upland, hard hardwoods equally as well as pine. INTRODUCTION The objective of this study was to analyze the productivity of a Ponsse Ergo harvester using an H73 head working in a mixed pine/hardwood stand in North Georgia on steep terrain. 1 Equipment History Ponsse was founded in Its parent company is based in Finland with offices in Sweden, France, Great Britain, Norway, and the United States. 2 The Ponsse group designs, manufactures, and markets forest machinery as well as information technology solutions for cut-to-length (CTL) harvesting. Ponsse manufactures two harvesters, three forwarders, three harvester heads, and a number of information technology packages. The H73 head, which was used in this study, is the largest harvester head that Ponsse manufactures. It s very versatile and can handle tree trunks up to 28 inches in diameter. The Ponsse Ergo Harvester has a standard forest machine control system known as Opticontrol, which integrates the 1 Funding for this research was made possible by a grant from the U.S. Forest Service and Ponsse USA. 2 The use of brand or model names is for reader convenience only and does not represent an endorsement by the authors, Auburn University, or the U.S. Forest Service.

2 diesel engine, measuring device, and crane control systems into an efficient entity. variables were defined in units of time per tree and are shown in Table 2. Figure 1. Ponsse Ergo Harvester This harvester also has a self-leveling cab feature, which keeps the cab and operator level even on steep slopes. Cut-To-Length Harvesting Background Cut-to-length (CTL) harvesting systems have proven to efficiently harvest a variety of tree sizes including first commercial thinnings. Studies have shown CTL to be a low impact form of harvesting. It provides minimal residual stand and site damage and requires less manpower and leaves fewer slash piles than traditional tree-length systems (Lanford and Stokes 1995). This system varies from the typical southern tree-length system because the trees are limbed and bucked into lengths at the stump, leaving limbs and tops evenly distributed throughout the tract (Stokes 1988). CTL provides a logging system which can be adapted to small tracts of timber, offers decreased environmental impact, and may better suit a landowner s long-term management plans (Holtzscher 1995). With social and aesthetic concerns becoming increasingly important, CTL operations stand to become the system of choice for the future. A solution to the many problems associated with thinning and small diameter tree harvest is CTL logging. CTL has many benefits to offer the landowner, contractor, and mill. This form of timber harvesting is not a new concept it has been practiced in Scandinavia, Canada, and the Northern U.S. for many years (Tufts and Brinker 1993). However, in the southeast the complexity of the machine, high initial cost, availability of financing, lack of service support, and resistance to change by local logging contractors has limited CTL mainly to thinning operations (Holtzscher 1995). STUDY METHODS Five one square chain plots were used for data collection. Each plot represented a different stand condition. Plots were selected based on the criterion of area, density, slope, and species. Plot conditions were as shown in Table 1. The harvester was video taped performing the operations of felling, limbing, and bucking on the five study plots. The videotapes supplied data for an elemental time study. Time elements included move time, swing time, fell time, process time, and number of bucks. Statistics Time elements were analyzed statistically using Number Crunching Statistical System (NCSS) Dependent 1 Clark and Saucier 1990, 2 Clark, Saucier, and McNab 1986 DATA ANALYSIS Each dependent variable was analyzed separately in order to form a model for its prediction. To give a statistical starting point, each dependent variable was defined as a function of possible independent variables. Basic models were: Move time/tree = Swing time/tree = Fell time/tree = Process time/tree = f (DBH, tons per acre, trees per acre, slope) f (DBH, tons per acre, trees per acre, slope) f (DBH, species, total height) f (DBH, total height, number of bucks, species) Total productive time/tree = f (DBH, total height, number of bucks, species, tons per acre, trees per acre, slope) Table 2. Variables Used in Statistical Analysis Dependent Variables Move time Swing time Fell time Process time Total productive time Table 1. Plot Conditions Plot Number Conditions Acreage Number of Trees Trees Per Acre Pine Trees Per Acre Hardwood Trees Per Acre Avg DBH (in) Avg Pine DBH (in) Avg Hardwood DBH (in) Avg Total Height (ft) Tons Per Acre Pine Tons Per Acre Hardwood Tons Per Acre Slope % Independent Variables DBH Total height Number of bucks Tons per acre Trees per acre Slope Species

3 Move Time Analysis 1. Trees per acre with a square-root inverse 2. Slope Best model to predict move time (minutes) per tree: = *TPASQIN *SLOPE *SLOPExTPASQIN INTERCEPT TPASQIN SLOPE SLOPExTPASQIN Intercept Model Error Total Swing Time Analysis 1. Slope Best model to predict swing time (minutes) per tree: = *SLOPE INTERCEPT SLOPE Intercept Model Error Total Fell Time Analysis 1. DBH Species difference after including DBH in the model was not significant. Best model to predict fell time (minutes) per tree: = *DBH INTERCEPT DBH Intercept Model Error Total Process Time Analysis When Total Height was Known 2. Total height Best model to predict process time (minutes) per tree when total height was known: = DBH 2 xht INTERCEPT DBH 2 xht Intercept Model Error Total Process Time Analysis When Total Height was Unknown

4 Total Best model to predict process time (minutes) per tree when total height was unknown: = *DBH 2 INTERCEPT DBH Intercept Model Error Total Total Productive Time Analysis When Total Height was Known 2. Total height 3. Slope 4. Trees per acre with a square-root inverse Best model to predict total productive time (minutes) per tree when total height was known: = *DBH 2 xht * SLOPExTPASQIN INTERCEPT DBH 2 xht SLOPExTPASQIN Intercept Model Error Total Productive Time Analysis When Total Height was Unknown 2. Slope 3. Trees per acre with a square-root inverse Best model to predict total productive time (minutes) per tree when total height was unknown: = *DBH * SLOPExTPASQIN INTERCEPT DBH SLOPExTPASQIN Intercept Model Error Total CONCLUSION For this study, slopes on the five study plots ranged from 0 to 46 percent. For all plots over 10 percent slope the harvester operator processed downhill. 46 percent slope is very extreme for ground based timber harvesting. Slope was found to be significant for predicting move time, swing time, and total productive time. Number of bucks, tons per acre, and species were not significant for predicting any of the dependent variables. It was not surprising that slope had an effect on the productivity of the harvester. Interestingly though, the analysis found species variation to be nonsignificant for predicting any of the analyzed dependent variables. That is to say that even on steep slopes, the Ponsse Ergo harvester harvested upland, hard hardwoods equally as well as pine. This might be explained by the fact that the landowner specified that large hardwood limbs did

5 not require processing so that they were left on the site to help prevent erosion. Had those large tops been merchandized, a species effect on processing time might have been significant. REFERENCES Clark, A., J.R. Saucier, and W.H. McNab Total- Tree Weight, Stem Weight, and Volume Tables for Hardwood Species in the Southeast. Georgia Forest Research Paper 60: Research Division, Georgia Forestry Commission. Clark, A., and J.R. Saucier Tables for Estimating Total-Tree Weights, Stem Weights, and Volumes of Planted and Natural Southern Pines in the Southeast. Georgia Forest Research Paper 79: Research Division, Georgia Forestry Commission. Holtzscher, M.A Cut-to-Length Logging: A Comparison of Thinning Systems. Auburn University Thesis. Lanford, B.L., and B.J. Stokes Comparison of Two Thinning Systems. Part 1. Stand and Site Impacts. Forest Products Journal. 45(5): Stokes, B.J Timber Harvesting Systems in the Southeastern United States. American Pulpwood Association Publication. (89-P-3): Tufts, R. A., and R.W. Brinker Valmet s Woodstar Series Harvesting System: A Case Study. Southern Journal of Applied Forestry 17(2):