%0 Research article
%T Performance modelling in forest operations through partial least square regression
%A Costa, Corrado
%A Menesatti, Paolo
%A Spinelli, Raffaele
%D 2012
%J Silva Fennica
%V 46
%N 2
%R doi:10.14214/sf.57
%U https://silvafennica.fi/article/57
%X Partial Least Square (PLS) regression is a recent soft-modelling  technique that generalizes and combines features from principal  component analysis (PCA) and multiple regression. It is particularly  useful when predicting one or more dependent variables from a large set  of independent variables, often collinear. The authors compared the  potential of PLS regression and ordinary linear regression for accurate  modelling of forest work, with special reference to wood chipping, wood  extraction and the continuous harvesting of short rotation coppice.  Compared to linear regression, PLS regression allowed producing models  that better fit the original data. What is more, it allowed handling  collinear variables, facilitating the extraction of sound models from  large amounts of field data obtained from commercial forest operations.  On the other hand, PLS regression analysis is not as easy to conduct,  and produces models that are less user-friendly. By producing  alternative models, PLS regression may provide additional – and not  alternative – ways of reading the data. Ideally, a comprehensive data  analysis could include both ordinary and PLS regression and proceed from  their results in order to get a better understanding of the phenomenon  under examination. Furthermore, the computational complexity of PLS  regression may stimulate interdisciplinary team-building, to the greater  benefit of scientific research within the field of forest operations.