Articles containing the keyword 'growth modelling'

A sensitive framework has been developed for modelling young radiata pine (Pinus radiata D. Don) survival, its growth and size class distribution, from time of planting to age 5 or 6 years. The data and analysis refer to the Central North Island region of New Zealand. The survival function is derived from a Weibull probability density function, to reflect diminishing mortality with the passage of time in young stands. An anamorphic family of trends was used, as very little between-tree competition can be expected in young stands. An exponential height function was found to fit best the lower portion of its sigmoid form. The most appropriate basal area/ha exponential function included an allometric adjustment which resulted in compatible mean height and basal area/ha models. Each of these equations successfully represented the effects of several establishment practices by making coefficients linear functions of site factors, management activities and their interactions. Height and diameter distribution modelling techniques that ensured compatibility with stand values were employed to represent the effects of management practices on crop variation. Model parameters for this research were estimated using data from site preparation experiments in the region and were tested with some independent data sets.

• Mason, E-mail: em@mm.unknown
• Whyte, E-mail: aw@mm.unknown

Norway’s most common tree species, Picea abies (L.) Karst. (Norway spruce), is often infected with Heterobasidion parviporum Niemelä & Korhonen and Heterobasidion annosum (Fr.) Bref.. Because Pinus sylvestris L. (Scots pine) is less susceptible to rot, it is worth considering if converting rot-infested spruce stands to pine improves economic performance. We examined the economically optimal choice between planting Norway spruce and Scots pine for previously spruce-dominated clear-cut sites of different site indexes with initial rot levels varying from 0% to 100% of stumps on the site. While it is optimal to continue to plant Norway spruce in regions with low rot levels, shifting to Scots pine pays off when rot levels get higher. The threshold rot level for changing from Norway spruce to Scots pine increases with the site index. We present a case study demonstrating a practical method (“Precision forestry”) for determining the tree species in a stand at the pixel level when the stand is heterogeneous both in site indexes and rot levels. This method is consistent with the concept of Precision forestry, which aims to plan and execute site-specific forest management activities to improve the quality of wood products while minimising waste, increasing profits, and maintaining environmental quality. The material for the study includes data on rot levels and site indexes in 71 clear-cut stands. Compared to planting the entire stand with a single species, pixel-level optimised species selection increases the net present value in almost every stand, with average increase of approximately 6%.

• Aza, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway https://orcid.org/0000-0002-6416-6697 E-mail: anfe@nmbu.no
• Kallio, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway E-mail: maarit.kallio@nmbu.no
• Pukkala, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@uef.fi
• Hietala, Norwegian Institute of Bioeconomy Research, PO Box 115, NO-1431 Ås, Norway E-mail: ari.hietala@nibio.no
• Gobakken, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway E-mail: terje.gobakken@nmbu.no
• Astrup, Norwegian Institute of Bioeconomy Research, PO Box 115, NO-1431 Ås, Norway E-mail: rasmus.astrup@nibio.no

• Henke, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: mhenke@uni-goettingen.de
• Huckemann, Institute of Mathematical Stochastics, University of Göttingen, 37077 Göttingen, Germany E-mail: huckeman@math.uni-goettingen.de
• Kurth, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: wk@informatik.uni-goettingen.de
• Sloboda, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: bslobod@web.de

• Fox, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: jcfox@unimelb.edu.au
• Bi, Forest Resources Research, New South Wales Department of Primary Industries, PO Box 100, Beecroft, NSW 2119 Australia E-mail: hb@nn.au
• Ades, School of Forest and Ecosystem Science, University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, Victoria 3121 Australia E-mail: pka@nn.au

• Huuskonen, University of Helsinki, Dept. of Forest Ecology, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: sh@nn.fi
• Hynynen, The Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jh@nn.fi