Efficiency drivers in harvesting operations in mixed Boreal stands: a Norwegian case study

Abstract

This paper uses Data Envelopment Analysis (DEA) to evaluate how the technical efficiency of forest harvesting operations is influenced by terrain conditions and forest attributes, in addition to exploring the existence of other influencing factors. To this end, 643 shift-level observations of harvesting operations on 253 distinct harvested sites were used. The aim of this study is to highlight the harvester's ability to maximize the outputs, represented by the number of assortments for various tree species, given inputs such as harvest volume, harvest time for various tree species, and distance traveled by the harvester. Operational environment variables such as harvest, or decision-making unit (DMU) size, shape, and terrain characteristics were included. We found large variations in efficiency scores, and that inefficient harvest operations could theoretically be improved by reducing input by up to ca. 80%. A second stage regression estimation was applied to identify which factors significantly affected inefficiency. It was found that the inefficiency decreases with increasing stem-volume for pine and broadleaves, increasing stand density, and increasing share of pulpwood and non-marketable timber, while it increases with the number of logs produced per tree (in broadleaves). Inefficiency increases also with an increasing ratio of actual travel distance to minimal travel distance. The study shows how adopting DEA methods in forest operations might be used in combining efficiency analysis and environmental factors, by identifying and measuring inefficiency due to, for example, difficult terrain.

The Effects of Natural and Economic Factors on the Financial Performance of Forest Management Units: The Example of Forest Districts of the State Forests National Forest Holding from Eastern Poland

“…According to Cubbage [94], harvesting costs often surpass actual wood growing costs. Aalmo et al [95] noted that ineffective harvesting operations can be improved, for instance by raising stand density or by increasing stem-volume for pine trees and broadleaves. Mixed felling systems usually entail higher costs [96].…”

Applications of DEA and SFA in benchmarking studies in forestry: state-of-the-art and future directions

“…Data entered as cost or revenues or biophysical quantities (e.g., volume of forest various forest products, volume stock, numbers of workers). A limited number of studies include environmental variables which may affect productivity (Aalmo and Baardsen 2015;Aalmo et al 2020) (see Supplementary Information Table S1).…”

“…The underlying assumption is that more inputs lead to more outputs and thus a positive correlation between inputs and outputs. Environmental variables (e.g., terrain ruggedness (Aalmo et al 2020)) represent operating conditions that directly affect the production process, but which are beyond the control of the DMUs. In the modeling, environmental variables can sometimes be dealt with as non-controllable inputs (if this facilitates the production) or outputs (if they complicate the production), while in other cases one must specifically account for these environmental conditions via the potential peer units such that DMUs are only benchmarked against other DMUs operating in similar or worse environments.…”

“…SFA usually requires a parametric functional form specification and distributional assumptions on the noise and inefficiency term, but has the advantage that it naturally handles noise in the data (Kumbhakar and Lovell 2003;Bogetoft and Otto 2011). The potential of such applications of benchmarking has so far merely been discussed in the research community (Aalmo and Baardsen 2015;Wang et al 2019;Obi and Visser 2020;Aalmo et al 2020).…”