Forest harvest operations productivity forecasting: a decisions tree approach

References

• Ackerman SA, Talbot B, Astrup R. 2022. The effect of tree and harvester size on productivity and harvester investment decisions. Int J For Eng. 33(1):22–32. doi: 10.1080/14942119.2021.1981046.
   Web of Science ®Google Scholar
• Arbuckle JL. 2010. IBM SPSS Amos 19 user’s guide. Crawfordville, FL: Amos Development Corporation; p. 635.
   Google Scholar
• Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. Wadsworth, Belmont, Ca: Taylor & Francis.
   Google Scholar
• Brewer J, Talbot B, Belbo H, Ackerman P, Ackerman S. 2018. A comparison of two methods of data collection for modelling productivity of harvesters: manual time study and follow-up study using on-board-computer stem records. Ann For Res. 61(1):109–124. doi: 10.15287/afr.2018.962.
   Google Scholar
• Bordón MR, Montagna JM, Corsano G. 2023. Operational transportation planning in the forest industry integrating bucking decisions. Ann Oper Res. 322(1):385–411. doi: 10.1007/s10479-022-04688-2.
   Web of Science ®Google Scholar
• Broz D, Durand G, Rossit D, Tohmé F, Frutos M. 2016. Strategic planning in a forest supply chain: a multigoal and multiproduct approach. Can J For Res. 47(3):297–307. doi: 10.1139/cjfr-2016-0299.
   Google Scholar
• Broz D, Rossit DA, Rossit DG, Cavallin C. 2018. The Argentinian forest sector: opportunities and challenges in supply chain management. Uncertain Supply Chain Manage. 6(4):375–392. doi: 10.5267/j.uscm.2018.1.001.
   Google Scholar
• Fritsche U, Brunori G, Chiaramonti D, Galanakis CM, Hellweg S, Matthews R, Panoutsou C. 2020. Future transitions for the bioeconomy towards sustainable development and a climate-neutral economy—knowledge synthesis final report. Publications Office of the European Union, Luxembourg, 95.
   Google Scholar
• Gao W, Qiu Q, Yuan C, Shen X, Cao F, Wang G, Wang G. 2022. Forestry big data: a review and Bibliometric Analysis. Forests. 13(10):1549. doi: 10.3390/f13101549.
   Web of Science ®Google Scholar
• Häggström C, Lindroos O. 2016. Human, technology, organization and environment–a human factors perspective on performance in forest harvesting. Int J For Eng. 27(2):67–78. doi: 10.1080/14942119.2016.1170495.
   Web of Science ®Google Scholar
• Hartsch F, Kemmerer J, Labelle ER, Jaeger D, Wagner T. 2021. Integration of harvester production data in German wood supply chains: legal, social and economic requirements. Forests. 12(4):460. doi: 10.3390/f12040460.
   Web of Science ®Google Scholar
• Hiesl P, Benjamin JG. 2013. Applicability of international harvesting equipment productivity studies in Maine, USA: a literature review. Forests. 4(4):898–921. doi: 10.3390/f4040898.
   Web of Science ®Google Scholar
• Hothorn T, Hornik K, Zeileis A. 2006. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 15(3):651–674. doi: 10.1198/106186006X133933.
   Web of Science ®Google Scholar
• Kemmerer J, Labelle ER. 2021. Using harvester data from on-board computers: a review of key findings, opportunities and challenges. Eur J For Res. 140(1):1–17. doi: 10.1007/s10342-020-01313-4.
   Web of Science ®Google Scholar
• Liski E, Jounela P, Korpunen H, Sosa A, Lindroos O, Jylhä P. 2020. Modeling the productivity of mechanized CTL harvesting with statistical machine learning methods. Int J For Eng. 31(3):253–262. doi: 10.1080/14942119.2020.1820750.
   Web of Science ®Google Scholar
• Loh WY, Shih YS. 1997. Split selection methods for classification trees. Stat Sin. 7:815–840.
   Web of Science ®Google Scholar
• Loh WY. 2014. Fifty years of classification and regression trees. Int Stat Rev. 82(3):329–348. doi: 10.1111/insr.12016.
   Web of Science ®Google Scholar
• Lopes ILE, Araújo LA, Miranda EN, Bastos TA, Gomide LR, Castro GP. 2022. A comparative approach of methods to estimate machine productivity in wood cutting. Int J For Eng. 33(1):43–55. doi: 10.1080/14942119.2021.1952520.
   Web of Science ®Google Scholar
• Martell DL, Gunn EA, Weintraub A. 1998. Forest management challenges for operational researchers. Eur J Oper Res. 104(1):1–17. doi: 10.1016/S0377-2217(97)00329-9.
   Web of Science ®Google Scholar
• Mederski PS, Schweier J, Đuka A, Tsioras P, Bont LG, Bembenek M. 2022. Mechanised harvesting of broadleaved tree species in Europe. Curr For Rep. 8(1):1–19. doi: 10.1007/s40725-021-00154-7.
   Web of Science ®Google Scholar
• Melander L, Ritala R. 2020. Separating the impact of work environment and machine operation on harvester performance. Eur J For Res. 139(6):1029–1043. doi: 10.1007/s10342-020-01304-5.
   Web of Science ®Google Scholar
• Murtiyoso A, Holm S, Riihimäki H, Krucher A, Griess H, Griess VC, Schweier J. 2023. Virtual forests: a review on emerging questions in the use and application of 3D data in forestry. Int J For Eng. 35(1):1–14. doi: 10.1080/14942119.2023.2217065.
   Web of Science ®Google Scholar
• Noordermeer L, Sørngård E, Astrup R, Næsset E, Gobakken T. 2021. Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees. Int J For Eng. 32(2):119–127. doi: 10.1080/14942119.2021.1899686.
   Web of Science ®Google Scholar
• Olivera A, Visser R, Acuna M, Morgenroth J. 2016. Automatic GNSS-enabled harvester data collection as a tool to evaluate factors affecting harvester productivity in a Eucalyptus spp. harvesting operation in Uruguay. Int J For Eng. 27(1):15–28. doi: 10.1080/14942119.2015.1099775.
   Web of Science ®Google Scholar
• Olivera Farias A, 2016. Exploring opportunities for the integration of GNSS with forest harvester data to improve forest management. PhD thesis. University of Canterbury, New Zealand.
   Google Scholar
• Oyarzo C, Rossit DA, Viana-Céspedes V, Olivera A. 2022, October. Discriminant method approach for harvesting forest operations. In 2022 International Conference on Data Analytics for Business and Industry (ICDABI); Bahrein. IEEE. p. 736–740.
   Google Scholar
• Proto AR, Macrì G, Visser R, Harrill H, Russo D, Zimbalatti G. 2018. Factors affecting forwarder productivity. Eur J For Res. 137(2):143–151. doi: 10.1007/s10342-017-1088-6.
   Web of Science ®Google Scholar
• Rossit DA, Olivera A, Céspedes VV, Broz D. 2019. A big data approach to forestry harvesting productivity. Comput Electron Agric. 161:29–52. doi: 10.1016/j.compag.2019.02.029.
   Web of Science ®Google Scholar
• Rossit D, Pais C, Weintraub A, Broz D, Frutos M, Tohme F. 2021. Stochastic forestry harvest planning under soil compaction conditions. J Environ Manage. 296:113157. doi: 10.1016/j.jenvman.2021.113157.
   PubMed Web of Science ®Google Scholar
• Santos A, Carvalho A, Barbosa-Póvoa AP, Marques A, Amorim P. 2019. Assessment and optimization of sustainable forest wood supply chains–A systematic literature review. For Policy Econ. 105:112–135. doi: 10.1016/j.forpol.2019.05.026.
   Web of Science ®Google Scholar
• Shan C, Bi H, Watt D, Li Y, Strandgard M, Ghaffariyan MR. 2021. A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations. J For Res (Harbin). 32(1):21–41. doi: 10.1007/s11676-019-01078-6.
   Web of Science ®Google Scholar
• Shikha C 2013. Survey paper on improved methods of ID3 decision tree classification. International Journal of Scientific and Research Publications 1–4.
   Google Scholar
• Spinelli R, de Arruda Moura AC. 2019a. Productivity and utilization benchmarks for chain flail delimber-debarkers-chippers used in fast-growing plantations. Croat J For Eng: J For Theory And App Of For Eng. 40(1):65–80.
   Web of Science ®Google Scholar
• Spinelli R, de Arruda Moura AC. 2019b. Decreasing the fuel consumption and CO2 emissions of excavator-based harvesters with a machine control system. Forests. 10(1):43. doi: 10.3390/f10010043.
   Web of Science ®Google Scholar
• Strandgard M, Mitchell R, Acuna M. 2017. Time consumption and productivity of a forwarder operating on a slope in a cut-to-length harvest system in a pinus radiata D. Don pine plantation. J For Sci. 63(7):324–330.
   Google Scholar
• Strandgard M, Walsh D, Acuna M. 2013. Estimating harvester productivity in Pinus radiata plantations using StanForD stem files. Scand J For Res. 28(1):73–80. doi: 10.1080/02827581.2012.706633.
   Google Scholar
• Strasser H, Weber C. 1999. On the asymptotic theory of permutation statistics. Math Methods Stat. 8:220–250.
   Google Scholar
• Schweier J, Klein ML, Kirsten H, Jaeger D, Brieger F, Sauter UH. 2020. Productivity and cost analysis of tower yarder systems using the Koller 507 and the Valentini 400 in southwest Germany. Int J For Eng. 31(3):172–183. doi: 10.1080/14942119.2020.1761746.
   Web of Science ®Google Scholar
• Therneau TM, Atkinson EJ. 1997. An introduction to recursive partitioning using the RPART routines. https://stat.ethz.ch/R-manual/R-patched/library/rpart/doc/longintro.pdf.
   Google Scholar
• Torresan C, Benito Garzón M, O’grady M, Robson TM, Picchi G, Panzacchi P, Kneeshaw D, Smith M, Marshall J, Wingate L. 2021. A new generation of sensors and monitoring tools to support climate-smart forestry practices. Can J For Res. 51(12):1751–1765. doi: 10.1139/cjfr-2020-0295.
   Google Scholar
• Wendler T, Gröttrup S. 2016. Data mining with SPSS modeler: theory, exercises and solutions. Switzerland: Springer.
   Google Scholar
• Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, Zhou ZH, Ng A, Liu B, Yu PS. 2008. Top 10 algorithms in data mining. Knowl Inf Syst. 14(1):1–37. doi: 10.1007/s10115-007-0114-2.
   Web of Science ®Google Scholar