Template-Type: ReDIF-Article 1.0 Author-Name: Juha Laitila Author-Name: Robert Prinz Author-Workplace-Name: Natural Resources Institute Finland (Luke), Production systems, Joensuu, Finland Author-Name: Lauri Sikanen Author-Workplace-Name: Natural Resources Institute Finland (Luke), Production systems, Joensuu, Finland Title: Selection of a chipper technology for small-scale operations - a Finnish case Abstract: The objective of this study was to determine the economic performance of alternative chipper choices for small-scale chipping based on unit cost (€ per chip-m3) and net present value (NPV) calculations. For the chipping cost and investment profitability analyses four tractor-powered professional or semi-professional disc chippers and two professional drum chippers mounted on a truck or powered by tractor were selected. Initial investment, operating costs, and the cost of outsourced chipping were the key elements for comparing the profitability of investment alternatives. The average purchase prices, cost factors, and technical details of the chipper units were acquired from machine dealers, specification sheets, a literature review, and interviews with chipping entrepreneurs. The results of the three tractor-powered professional chippers involved in the comparison were very close to each other. The profitable running of a truck-mounted drum chipper calls for high annual chipping volumes: the chipper type is therefore a feasible choice for an entrepreneur in large-scale chipping. Semi-professional disc chippers offer lower investment costs, but their economic feasibility is relatively poor. Keywords: Investment, decision making, unit cost, chipping, entrepreneurship, productivity Journal: Journal of Forest Science Pages: 121-133 Volume: 65 Issue: 4 Year: 2019 DOI: 10.17221/26/2019-JFS File-URL: http://jfs.agriculturejournals.cz/doi/10.17221/26/2019-JFS.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/jfs-201904-0001.txt Handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:26-2019-JFS Template-Type: ReDIF-Article 1.0 Author-Name: Nguyen Thanh Tuan Author-Workplace-Name: Department of Forestry, Vietnam National University of Forestry, Dong Nai, Vietnam Author-Name: Tai Tien Dinh Author-Workplace-Name: Institute of Resources and Environment, Hue University, Hue, Vietnam Author-Name: Shen Hai Long Author-Workplace-Name: School of Forestry, Northeast Forestry University, Harbin, P.R. China Title: Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models Abstract: Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height. Keywords: Forest measurement, nonlinear growth functions, artificial intelligence technology, Korean pine plantation Journal: Journal of Forest Science Pages: 134-143 Volume: 65 Issue: 4 Year: 2019 DOI: 10.17221/5/2019-JFS File-URL: http://jfs.agriculturejournals.cz/doi/10.17221/5/2019-JFS.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/jfs-201904-0002.txt Handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:5-2019-JFS Template-Type: ReDIF-Article 1.0 Author-Name: Millana Bürger Pagnussat Author-Workplace-Name: Department of Forest Engineering, State University of the Center-West - UNICENTRO, Irati, Paraná, Brazil Author-Name: Eduardo da Silva Lopes Author-Workplace-Name: Department of Forest Engineering, State University of the Center-West - UNICENTRO, Irati, Paraná, Brazil Author-Name: Rachael D. Seidler Author-Workplace-Name: Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, USA Title: Behavioural profile effect of forestry machine operators in the learning process Abstract: A lack of efficient operators for wood harvesting machines poses a great challenge. Here, our objective was to evaluate the effect of the behavioural profile on the productive efficiency of forwarder operators. The study was carried out in a Brazilian company, with a sample of 10 operators. A profile evaluation characterized the reference profile, comparing with the profile of the operators studied. The operators were evaluated through their productive efficiency, for 11 months to track learning curves. The results showed that operators must be attentive to details, deadlines, rules, be patient and a moderate initiative taker. The operators were classified into two behavioural profiles, class 1 appropriate to the position and class 2 with some inappropriate points. The productive efficiency of the operators increased during the training, with the profile operators 1 and 2 reaching the targets set by the company in the fifth and seventh month, respectively. The difference in the average productive efficiency between the operators of profile 1 and 2 during the training process was 19%. Keywords: wood, operator recruitment, learning curves, productivity Journal: Journal of Forest Science Pages: 144-149 Volume: 65 Issue: 4 Year: 2019 DOI: 10.17221/27/2019-JFS File-URL: http://jfs.agriculturejournals.cz/doi/10.17221/27/2019-JFS.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/jfs-201904-0003.txt Handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:27-2019-JFS Template-Type: ReDIF-Article 1.0 Author-Name: Ding Xiong Author-Name: Lu Yan Author-Workplace-Name: School of Electrical and Information Engineering, Hunan International Economics University, Changsha, China Title: Early smoke detection of forest fires based on SVM image segmentation Abstract: A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection. Keywords: Support Vector Machines (SVM), single frame, horizon detection, superpixel merging, forest fire prevention Journal: Journal of Forest Science Pages: 150-159 Volume: 65 Issue: 4 Year: 2019 DOI: 10.17221/82/2018-JFS File-URL: http://jfs.agriculturejournals.cz/doi/10.17221/82/2018-JFS.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/jfs-201904-0004.txt Handle: RePEc:caa:jnljfs:v:65:y:2019:i:4:id:82-2018-JFS