Stand-level growth models for long-term projections of the main species groups in Norway

References

• Allen MG, Antón-Fernández C, Astrup R. 2020. A stand-level growth and yield model for thinned and unthinned managed Norway spruce forests in Norway. Scandinavian Journal of Forest Research. 35(5-6):238–251. DOI: https://doi.org/10.1080/02827581.2020.1773525.
   Web of Science ®Google Scholar
• Anastasov IZ. 2011. Evaluation of basal area projection models for unthinned and thinned central Appalachian hardwood forest stands. West Virginia University Libraries; [Accessed 3/8/2021]. https://researchrepository.wvu.edu/etd/3302.
   Google Scholar
• Anta MB, Dorado FC, Diéguez-Aranda U, González Á, Juan G, Parresol BR, Soalleiro RR. 2006. Development of a basal area growth system for maritime pine in northwestern Spain using the generalized algebraic difference approach. Can. J. For. Res. 36(6):1461–1474. DOI: https://doi.org/10.1139/x06-028.
   Google Scholar
• Bailey RL, Ware KD. 1983. Compatible basal-area growth and yield model for thinned and unthinned stands. Can. J. For. Res. 13(4):563–571. DOI: https://doi.org/10.1139/x83-082.
   Web of Science ®Google Scholar
• Bates DM, Watts DG. 1988. Nonlinear regression analysis and Its applications. Hoboken: John Wiley & Sons, Inc (Wiley Series in Probability and Statistics). [Accessed 3/8/2021]. https://doi.org/http://doi.org/10.1002/9780470316757.
   Google Scholar
• Blingsmo K. 1984. Diameter increment functions for stands of birch, Scots pine, and Norway spruce. Ås: Norwegian Forest Research Institute. In 0333-001X, p. 22.
   Google Scholar
• Borders BE. 1989. Systems of equations in forest stand modeling. For Sci. 35(2):548–556. DOI: https://doi.org/10.1093/forestscience/35.2.548.
   Google Scholar
• Braastad H. 1966. Volumtabeller for bjørk. Meddelelser fra Det Norske Skogforsøksvesen. 21:23–78.
   Google Scholar
• Braastad H. 1974. Diametertilvekstfunksjoner for gran. Meddelelser fra Norsk Institutt for Skogforskning, 31/1.
   Google Scholar
• Braastad H. 1975. Produksjonstabeller og tilvekstmodeller for gran. Meddelelser fra Norsk Institutt for Skogforskning, 31/9.
   Google Scholar
• Braastad H. 1977. Tilvekstmodellprogram for bjørk. Rapport fra Norsk Institutt for Skogforskning, 1/77.
   Google Scholar
• Braastad H. 1980. Tilvekstmodellprogram for furu. Meddelelser fra Norsk Institutt for Skogforskning, 35/5.
   Google Scholar
• Braastad H. 1982. Naturlig avgang i granbestand. Rapport fra Norsk institutt for skogforskning 12/82.
   Google Scholar
• Brantseg A. 1967. Furu sønnafjells. Kubering av stående skog Funksjoner og tabeller. Meddelelser fra Det Norske Skogforsøksvesen. 22:695–739.
   Google Scholar
• Breidenbach J, Granhus A, Hylen G, Eriksen R, Astrup R. 2020a. A century of National Forest Inventory in Norway – informing past, present, and future decisions. For. Ecosyst. 7(1):46. DOI: https://doi.org/10.1186/s40663-020-00261-0.
   PubMed Web of Science ®Google Scholar
• Breidenbach J, Waser LT, Debella-Gilo M, Schumacher J, Rahlf J, Hauglin M, et al. 2020b. National mapping and estimation of forest area by dominant tree species using sentinel-2 data. Can. J. For. Res. 51(3):1–15. DOI: https://doi.org/10.1139/cjfr-2020-0170.
   Google Scholar
• Buchman RG, Shifley SR. 1983. Guide to evaluating forest growth projection systems. Journal of Forestry. 81(4):232–254. DOI: https://doi.org/10.1093/jof/81.4.232.
   Web of Science ®Google Scholar
• Burkhart HE, Tomé M. 2012. Modeling forest trees and stands. Dordrecht: Springer Netherlands. [Accessed 4/26/2021]. http://link.springer.com/https://doi.org/10.1007/978-90-481-3170-9.
   Google Scholar
• Cieszewski CJ, Bailey RL. 2000.: Generalized algebraic difference approach: theory based derivation of dynamic site equations with polymorphism and variable asymptotes. For Sci. 46(1):116–126. DOI: https://doi.org/10.1093/forestscience/46.1.116.
   Google Scholar
• Clutter JL, Fortson JC, Pienaar LV, Brister GH, Bailey RL. 1983. Timber management, A quantitative approach. New York: Wiley.
   Google Scholar
• Coble DW. 2009. A new whole-stand model for unmanaged loblolly and slash pine plantations in east texas. South J Appl For. 33(2):69–76. DOI: https://doi.org/10.1093/sjaf/33.2.69.
   Google Scholar
• Diéguez-Aranda U, Burkhart HE, Rodríguez-Soalleiro R. 2005a. Modeling dominant height growth of radiata pine (Pinus radiata D. Don) plantations in north-western Spain. For Ecol Manag. 215(1):271–284. DOI: https://doi.org/10.1016/j.foreco.2005.05.015.
   Web of Science ®Google Scholar
• Diéguez-Aranda U, Castedo-Dorado F, Álvarez-González JG, Rodríguez-Soalleiro R. 2005b. Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain. Eur J Forest Res. 124(2):143–153. DOI: https://doi.org/10.1007/s10342-004-0043-5.
   Web of Science ®Google Scholar
• Dunn PK, Smyth GK. 1996. Randomized quantile residuals. J Comput Graph Stat. 5(3):236–244. DOI: https://doi.org/10.1080/10618600.1996.10474708.
   Google Scholar
• Eid T. 2001. Models for prediction of basal area mean diameter and number of trees for forest stands in south-eastern Norway. Scandinavian Journal of Forest Research. 16(5):467–479. DOI: https://doi.org/10.1080/02827580152632865.
   Web of Science ®Google Scholar
• Eid T, Hobbelstad K. 2000.: AVVIRK-2000: A large-scale Forestry scenario model for long-term investment, income and harvest analyses. Scandinavian Journal of Forest Research. 15(4):472–482. DOI: https://doi.org/10.1080/028275800750172736.
   Web of Science ®Google Scholar
• Elzhov TV, Mullen KM, Spiess A-N, Bolker B. 2016. minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds. [Accessed 3/8/2021]. https://CRAN.R-project.org/package=minpack.lm.
   Google Scholar
• Hasenauer H, Burkhart HE, Amateis RL. 1997. Basal area development in thinned and unthinned loblolly pine plantations. Can. J. For. Res. 27(2):265–271. DOI: https://doi.org/10.1139/x96-163.
   Web of Science ®Google Scholar
• Hobbelstad K, Hofstad O. 1988. AVVIRK3, a model for long-term forest planning. Rep. Norw. For. Res. Inst. 41:503–516.
   Google Scholar
• Hoen HF, Eid T. 1990.: A model for analysis of treatment strategies for a forest applying standvice simulations and linear programming [En modell for analyse av behandlingsalternativer for en skog ved bestandssimulering og lineær programmering]. Report. 9:90.
   Google Scholar
• Hoen HF, Gobakken T. 1997. Brukermanual for Bestandssimulatoren GAYA v1.20. Department of Forest Sciences, Agricultural University of Norway, 59 pp. Internal report.
   Google Scholar
• Hynynen J, Ojansuu R, Hökkä H, Siipilehto J, Salminen H, Haapala P. 2002. Models for predicting stand development in MELA system: Metsäntutkimuslaitos. http://urn.fi/URN:ISBN:951-40-1815-X.
   Google Scholar
• Kleiber C, Zeileis A. 2016. Visualizing count data regressions using rootograms. Am Stat. 70(3):296–303. DOI: https://doi.org/10.1080/00031305.2016.1173590.
   Web of Science ®Google Scholar
• Kuehne C, McLean JP, Maleki K, Antón-Fernández C, Astrup R. 2022. A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway. Silva Fenn. 56(1). DOI: https://doi.org/10.14214/sf.10627.
   Google Scholar
• Kuehne C, Weiskittel AR, Fraver S, Puettmann KJ. 2015. Effects of thinning-induced changes in structural heterogeneity on growth, ingrowth, and mortality in secondary coastal douglas-fir forests. Can. J. For. Res. 45(11):1448–1461. DOI: https://doi.org/10.1139/cjfr-2015-0113.
   Google Scholar
• Lappi J. 1992. JLP: a linear programming package for management planning. Helsinki: Finnish Forest Research Institute. (Metsäntutkimuslaitoksen tiedonantoja).
   Google Scholar
• Liu J, Burkhart HE, Amateis RL. 1995. Projecting crown measures for loblolly pine trees using a generalized thinning response function. For Sci. 41(1):43–53. DOI: https://doi.org/10.1093/forestscience/41.1.43.
   Google Scholar
• McCullagh A, Black K, Nieuwenhuis M. 2017. Evaluation of tree and stand-level growth models using national forest inventory data. Eur J Forest Res. 136(2):251–258. DOI: https://doi.org/10.1007/s10342-017-1025-8.
   Web of Science ®Google Scholar
• McTague JP, O'Loughlin D, Roise JP, Robison DJ, Kellison RC. 2008. The SOHARC model system for growth and yield of southern hardwoods. South J Appl For. 32(4):173–183. DOI: https://doi.org/10.1093/sjaf/32.4.173.
   Google Scholar
• Palahí M, Tomé M, Montero G, Miina J. 2002. Stand-level yield model for Scots pine (Pinus sylvestris L.) in north-east Spain. Investigación Agraria. Sistemas y Recursos Forestales. 11(2):409–424.
   Google Scholar
• Pienaar LV, Rheney JW. 1995. Modeling Stand level growth and yield Response to silvicultural treatments. For Sci. 41(3):629–638. DOI: https://doi.org/10.1093/forestscience/41.3.629.
   Google Scholar
• Pienaar LV, Shiver BD. 1986. Basal area prediction and projection equations for pine plantations. For Sci. 32(3):626–633. DOI: https://doi.org/10.1093/forestscience/32.3.626.
   Google Scholar
• Pukkala T, Lähde E, Laiho O. 2011. Using optimization for fitting individual-tree growth models for uneven-aged stands. Eur J Forest Res. 130(5):829–839. DOI: https://doi.org/10.1007/s10342-010-0475-z.
   Web of Science ®Google Scholar
• Pukkala T, Lähde E, Laiho O. 2013. Species interactions in the dynamics of even- and uneven-aged boreal forests. Journal of Sustainable Forestry. 32(4):371–403. DOI: https://doi.org/10.1080/10549811.2013.770766.
   Google Scholar
• R Core Team. 2021. European Environment Agency. [Accessed 3/8/2021]. https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006.
   Google Scholar
• Sharma M, Smith M, Burkhart HE, Amateis RL. 2006. Modeling the impact of thinning on height development of dominant and codominant loblolly pine trees. Ann. For. Sci. 63(4):349–354. DOI: https://doi.org/10.1051/forest:2006015.
   Web of Science ®Google Scholar
• Sharma RP, Brunner A. 2017. Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway. Scandinavian Journal of Forest Research. 32(6):501–514. DOI: https://doi.org/10.1080/02827581.2016.1269944.
   Web of Science ®Google Scholar
• Sharma RP, Brunner A, Eid T, Øyen B-H. 2011. Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors. For Ecol Manag. 262(12):2162–2175. DOI: https://doi.org/10.1016/j.foreco.2011.07.037.
   Web of Science ®Google Scholar
• Short E, Austin B, Harold E. 1992. Predicting crown-height increment for thinned and unthinned Loblolly pine plantations. For Sci. 38(3):594–610. DOI: https://doi.org/10.1093/forestscience/38.3.594.
   Google Scholar
• Socha J, Tymińska-Czabańska L, Grabska E, Orzeł S. 2020. Site index models for main forest-forming tree species in Poland. Forests. 11(3):19. DOI: https://doi.org/10.3390/f11030301.
   Web of Science ®Google Scholar
• Strand L, Braastad H. 1967. Høydekurver for bjørk. Meddelelser fra det Norske Skogforsøksvesen. 22:291–302.
   Google Scholar
• Thapa R, Burkhart HE. 2015. Modeling stand-level mortality of loblolly pine (Pinus taeda L.) using stand, climate, and soil variables. For Sci. 61(5):834–846. DOI: https://doi.org/10.5849/forsci.14-125.
   Google Scholar
• Tomter SM, Hylen G, Nilsen JE. 2010. Norway. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts RE, editors. National forest inventories: pathways for common reporting. Heidelberg, Dordrecht: Springer Science; p. 411–422. [Accessed 3/2/2021]. http://site.ebrary.com/id/10355992.
   Google Scholar
• Tveite B. 1967. Sambandet mellom grunnflateveid middelhøyde og noen andre bestandshøyder i gran- og furuskog. Meddelelser fra Det Norske Skogforsøksvesen. 22:483–538.
   Google Scholar
• Tveite B. 1976. Bonitetskurver for furu. Intern rapport. [Unpublished].
   Google Scholar
• Tveite B. 1977. Bonitetskurver for gran. Norsk Institute for Skogforskning. 33(1):88.
   Google Scholar
• Tveite B, Braastad H. 1984. Bonitering av gran, furu og bjørk. Ås: NLH, Norsk institutt for skogforskning.
   Google Scholar
• Vanclay JK. 1994. Modelling forest growth and yield: applications to mixed tropical forests. Wallingford: CAB International.
   Google Scholar
• Vestjordet E. 1967. Funksjoner og tabeller for kubering av stående gran. Meddelelser fra Det Norske Skogforsøksvesen. 22:539–574.
   Google Scholar
• Viken KO. 2018. Landsskogtakseringens feltinstruks: NIBIO (4(6)). www.xide.no.
   Google Scholar
• Wagenmakers E-J, Farrell S. 2004. AIC model selection using Akaike weights. Psychon Bull Rev. 11(1):192–196. DOI: https://doi.org/10.3758/BF03206482.
   PubMed Web of Science ®Google Scholar
• Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK. 2011. Forest Growth and Yield Modeling: vanclay/forest growth and yield modeling. Chichester: John Wiley & Sons, Ltd. [Accessed 4/26/2021]. https://doi.org/http://doi.org/10.1002/9781119998518.
   Google Scholar
• Woollons R. 1998. Even-aged stand mortality estimation through a two-step regression process. For Ecol Manag. 105:189–195. doi:https://doi.org/10.1016/S0378-1127(97)00279-X
   Web of Science ®Google Scholar
• Yue C, Kohnle U, Hein S. 2008.combining tree- and stand-level models: A new approach to growth prediction. For Sci. 54(5):553–566. DOI: https://doi.org/10.1093/forestscience/54.5.553.
   Google Scholar
• Zeileis A, Kleiber C. 2016. countreg: Count Data Regression. R package version 0.1-5/r106. http://R-Forge.R-project.org/projects/countreg/.
   Google Scholar
• Zhao D, Kane M, Markewitz D, Teskey R, Clutter M. 2015. Additive tree biomass equations for midrotation Loblolly pine plantations. For Sci. 61(4):613–623. DOI: https://doi.org/10.5849/forsci.14-193.
   Google Scholar