1990
DOI: 10.5558/tfc66271-3
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Modelling the Sustainability of Forest Production and Yield for a Changing and Uncertain Future

Abstract: DedicationThis paper is dedicated, with great respect, to the memory of Dr. Peter J. Rennie, who has been an inspiration to those working on long-term site productivity since his pioneering work in UK in the early 1950's. His more recent concerns over acid rain and the greenhouse effect provided leadership to forest scientists working on these topics. He will be missed, but remembered with respect.

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Cited by 46 publications
(20 citation statements)
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“…They may be an appropriate method for predicting short-term yield for time scales over which historical growth conditions are not expected to change significantly. However, empirical growth and yield models are not robust, for example, for analysing the consequences of climatic changes or environmental stress (Kimmins, 1990;Shugart et al, 1992).…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…They may be an appropriate method for predicting short-term yield for time scales over which historical growth conditions are not expected to change significantly. However, empirical growth and yield models are not robust, for example, for analysing the consequences of climatic changes or environmental stress (Kimmins, 1990;Shugart et al, 1992).…”
Section: Introductionmentioning
confidence: 99%
“…Predicting the potential effects of future changes in global environment (such as climate, land-use, fire disturbance, and forest harvesting) on the sustainability of forest ecosystems will require forest resource managers to make use of forest simulation models (Peng, 2000a). Basic approaches to modelling forest growth and C dynamics include empirical, mechanistic, and hybrid forest simulation models, each with their advantages and limitations (Landsberg, 1986;Kimmins, 1990;Mohren et al, 1994;Landsberg and Coops, 1999;Mäkelä et al, 2000;Peng, 2000a,b).…”
Section: Introductionmentioning
confidence: 99%
“…Despite all these positive features, because of the inherent assumption of constant growing conditions, empirical models have limited predictive capability when changes occur in the tree growing environment, management conditions, or disturbance regimes (Kimmins 1990, Monserud 2003, Pinjuv et al 2006). This limitation is caused by the fact that empirical models rely on historical data in their elaboration (Johnsen et al 2001, Landsberg 2003a.…”
Section: Introductionmentioning
confidence: 99%
“…In resource management, it is necessary not only to identify patterns (e.g., how many forest stands of what age and species composition are present in a management area) but also to understand how these patterns were formed and to predict how they will change in the future (Kimmins 1990). In the case of environmentally dependent processes, physiology research will often provide such understanding and will be useful in making predictions.…”
Section: Why Should Forest Managers Support Physiology Research At Mumentioning
confidence: 99%