Performance modelling in forest operations through partial least square regression

Silva Fennica

Volumn 46, Issue 2, 2012, Pages 241-252

  Costa, Corrado ^a   Menesatti, Paolo ^a   Spinelli, Raffaele ^b  

^a ISTITUTO NAZIONALE DELLA NUTRIZIONE   (Italy)

^b CNR IVALSA   (Italy)

Author keywords

Chipping; Harvesting; Productivity; Skidding

Indexed keywords

EXTRACTION; FORESTRY; HARVESTING; PRINCIPAL COMPONENT ANALYSIS; PRODUCTIVITY; SKIDDING;

CHIPPING; INDEPENDENT VARIABLES; INTERDISCIPLINARY TEAMS; MULTIPLE REGRESSIONS; PARTIAL LEAST-SQUARE REGRESSION; PERFORMANCE MODELLING; SCIENTIFIC RESEARCHES; SHORT ROTATION COPPICE;

REGRESSION ANALYSIS;

COPPICE; DATA INTERPRETATION; FORESTRY MODELING; FORESTRY PRACTICE; FORESTRY PRODUCTION; INTERDISCIPLINARY APPROACH; LEAST SQUARES METHOD; MULTIPLE REGRESSION; PERFORMANCE ASSESSMENT; PRINCIPAL COMPONENT ANALYSIS; PROFITABILITY; RESEARCH WORK; TIMBER HARVESTING;

CHIPPING; FORESTRY; HARVESTING; PRODUCTIVITY; REGRESSION ANALYSIS; SKIDDING;

EID: 84861475741     PISSN: 00375330     EISSN: None     Source Type: Journal    
DOI: 10.14214/sf.57     Document Type: Article

References (44)

1. Abdi, H. 2003. Partial Least Squares (PLS) regression. In: Lewis-Beck M., Bryman, A. & Futing T. (eds.). Encyclopedia of social sciences research methods. Thousand Oaks (CA - USA).
2. Antonucci, F., Menesatti, P., Holdenb, N., Canali, E., Giorgi, S., Maienza, A. & Stazi, S. 2011. Hyper-spectral visible and near infrared determination of copper concentration in agricultural polluted soils. Communications in Soil Science and Plant Analysis. (In press).
3. Bagozzi, R. & Yi, Y. 1994. Advanced topics in structural equation models. In: Bagozzi, R. (ed.). Advanced methods of marketing research. Blackwell, Oxford. 51 p.
4. Bergstrand, K. 1987. Planning and analysis of time studies on forest technology. The Forest Operations Institute of Sweden. Report 17. 58 p.
5. Björheden, R. 1988. New work-study methods help to decide processing technique in logging. Scandanavian Journal of Forest Research 3: 569-574.
6. Capoccioni, F., Costa, C., Aguzzi, J., Menesatti, P., Lombarte, A. & Ciccotti, E. 2011. Ontogenetic and environmental effects on otolith shape variability in three Mediterranean European eel (Anguilla anguilla, L.) populations. Journal of Experimental Marine Biology and Ecology 397: 1-7.
7. Chiorescu, S. & Grönlund, A. 2001. Assessing the role of the harvester within the forestry-wood chain. Forest Products Journal 51: 77-84.
8. Costa, C., Angelini, C., Scardi, M., Menesatti, P. & Utzeri, C. 2009. Using image analysis on the ventral colour pattern in Salamandrina perspicillata (Savi, 1821) (Amphibia, Salamandridae) to discriminate among populations. Biological Journal of the Linnean Society 96: 35-43.
9. Costa, C, Vandeputte, M., Antonucci, F., Boglione, C., Menesatti, P., Cenadelli, S., Parati, K., Chavanne, H. & Chatain, B. 2010. Genetic and environmental influences on shape variation in the European sea bass (Dicentrarchus labrax). Biological Journal of the Linnean Society 101: 427-436.
10. De Jong, S. 1993. SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 18: 251-263.
11. De Maesschalck, R., Estienne, F., Verdú-Andrés, J., Candolfi, A., Centner, V., Despagne, F., Jouan-Rimbaud, D., Walczak, B., Massart, D.L., de Jong, S., de Noord, O.E., Puel, C. & Vandeginste, B.M.G. 1999. The development of calibration models for spectroscopic data using principal component regression. Internet Journal of Chemistry 2. p. 19.
12. Eliasson, L. 1999. Simulation of thinning with a single-grip harvester. Forest Science 45: 26-34.
13. Frank, I. & Friedman J. 1993. A statistical view of chemometrics regression tools. Technometrics 35: 109-148.
14. Freedman, D., Pisani, R. & Purves, R. 2007. Statistics. 4th edition. W.W. Norton, Inc. New York. 697 p.
15. Geladi, P. & Kowalski, B. 1986. Partial least square regression: a tutorial. Analytica Chemica Acta 35: 1-17.
16. Harrop-Galvao, R., Ugulino-Araujo, M., Emidio-Jose, M., Coelho-Pontes, M, Cirino-Silva, E. & Bezerra-Saldanha, T. 2005. A method for calibration and validation subset partitioning. Talanta 67: 736-740.
17. Helland, I. 1990. Pls regression and statistical models. Scandivian Journal of Statistics, 17: 97-114.
18. Hoskuldson, A. 1988. Pls regression methods. Journal of Chemometrics 2: 211-228.
19. Howard, A. 1989. A sequential approach to sampling design for time studies of cable yarding operations. Canadian Journal of Forest Research 19: 973-980.
20. Kresta, J.1992 The application of Partial Least Squares to problems in chemical engineering. Ph. D. thesis at McMaster University, Canada. 215 p.
21. Lipp, M. 1996. Comparison of PLS, PCR and MLR for the quantitative determination of foreign oils and fats in butter fats of several European countries by their triglyceride composition. Zeitschrift für Lebensmittel Untersuchung und Forschung A 202(3): 193-198.
22. Martens, H. & Naes, T. 1989. Multivariate calibration. London, Wiley.
23. McIntosh, A., Bookstein, F., Haxby, J. & Grady, C.L. 1996. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3: 143-157.
24. Nakagawa, M., Hayashi, N. & Narushima, T. 2010. Effect of tree size on time of each work element and processing productivity using an excavator-based single-grip harvester or processor at a landing. Journal of Forest Research 15: 226-233.
25. Nsofor, G.C. 2006. Comparative analysis of predictive data-mining techniques. MSc thesis, University of Tennessee, Knoxville, USA.
26. Nurminen, T., Korpunen, H. & Uusitalo, J. 2006. Time consumption analysis of mechanized cut-to-length harvesting systems. Silva Fennica 40: 335-363.
27. Nuutinen, Y., Väätäinen, K., Heinonen, J., Asikainen, A. & Röser, D. 2008. The accuracy of manually recorded time study data for harvester operation shown via simulator screen. Silva Fennica 42: 63-72.
28. Olsen, E., Hossain, M. & Miller, M. 1998. Statistical comparison of methods used in harvesting work studies. Oregon State University, Forest Research Laboratory, Corvallis, OR. Research Contribution 23. 31 p.
29. Ovaskainen, H., Uusitalo, J. & Väätäinen, K. 2004. Characteristics and significance of a harvester operator's working technique in thinnings. International Journal of Forest Engineering 15: 67-77.
30. Pulos, S. & Rogness, N. 1995. Soft modeling and special education. Remedial and Special Education 16: 184-192.
31. Samset, I. 1990. Some observations on time and performance studies in forestry. Communications of the Norwegian Forest Research Institute 43(5). 80 p.
32. Spinelli, R. & Hartsough, B. 2001. A survey of Italian chipping operations. Biomass and Bioenergy 21: 433-444.
33. Spinelli, R & Magagnotti, N. 2009. A tool for productivity and cost forecasting of decentralised wood chipping. Forest Policy and Economics 12(3): 194-198. doi:10.1016/j.forpol.2009.10.002.
34. Spinelli, R & Magagnotti, N. 2011a. The effects of introducing modern technology on the financial, labour and energy performance of forest operations in the Italian Alps. Forest Policy and Economics 13: 520-524.
35. Spinelli, R & Magagnotti, N. 2011b. Wood extraction with farm tractor and sulky: estimating productivity, cost and energy consumption. Small Scale Forestry. DOI 10.1007/s11842-011-9169-8.
36. Spinelli, R, Nati, C. & Magagnotti, N. 2009. Using modified foragers to harvest short-rotation poplar plantations. Biomass and Bioenergy 33: 817-821.
37. Spinelli, R, Hartsough, B. & Magagnotti, N. 2010. Productivity standards for harvesters and processors in Italy. Forest Products Journal 60: 226-235.
38. Spinelli, R, Magagnotti, N., Paletto, G. & Preti, C. 2011 Determining the impact of some wood characteristics on the performance of a mobile chipper. Silva Fennica 45(1): 85-95.
39. Suadicani, K. & Fjeld, D. 2001. Single-tree and group selection in montane Norway spruce stands: factors influencing operational efficiency. Scandinavian Journal of Forest Research 16: 79-87.
40. Viscarra-Rossel, R., Taylor, H. & McBratney, A. 2007. Multivariate calibration of hyperspectral g-ray energy spectra for proximal soil sensing. European Journal of Soil Science 58(1): 343-353.
41. Visser, R. & Spinelli, R. 2011. Determining the shape of the productivity function for mechanised felling and felling-processing. Journal of Forest Research. DOI 10.1007/s10310-011-0313-2.
42. Visser, R & Stampfer, K. 1998. Cable extraction of harvester felled thinnings: an Austrian case study. Journal of Forest Engineering 9: 39-46.
43. Williams, P. 1987. Variables affecting near-infrared reflectance spectroscopic analysis. In: Williams, P. & Norris, K. (eds.). Near-infrared technology in the agricultural and food industries. American Association of Cereal Chemists, St Paul, Minnesota, USA. p. 143-166.
44. Wold, H. 1966. Estimation of principal components and related models by iterative least squares. In: Krishnaiaah, P.R. (ed.). Multivariate analysis. New York: Academic Press. p. 391-420.