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The application of landscape productivity and environmental modelling to improve plantation site selection and yield prediction

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Mummery, Daryl Clarke 2009 , 'The application of landscape productivity and environmental modelling to improve plantation site selection and yield prediction', PhD thesis, University of Tasmania.

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Abstract

The area planted to Eucalyptus globulus (Labill.) in southern Australia has increased
rapidly. However, robust site selection and productivity prediction models have only
become available recently for this species. The silvicultural and management
questions being explored with these models cover a range of temporal and spatial
scales. The models themselves are complex, data intensive and highly non-linear in
their function. This raises a number of important issues that affect model application.
These issues are explored with a series of applied examples.
Firstly, what is the effect of patchy input data of limited extent? Models designed
using sampling plot data on which soils data are recorded are now applied across broad
landscape extents to make site selection decisions and regional productivity estimates.
The work in this thesis shows in separate examples how terrain analysis can be used to
supplement input surfaces, including surfaces related to waterlogging and soil
nutrition. An approach is developed for dealing with poorly defined soils variables
such as soil depth and used to categorise regional forest productivity based not only on
the expected yield but the co-efficient of variation in that yield. In a further example
this approach is used to define appropriate site surveying procedures for plantation
land assessment.
In addition to examining the effect of model predictive accuracy on the quality of
spatial inputs, input data grain size is examined to identify the scale sensitivity of the
model. This is important because the same forest growth models are used to predict
the yield of individual forest plots and to make continental scale predictions. The
results indicate that forest productivity models like PROMOD are not scale dependent
and even with the use of broad-scale input data provide unbiased estimates of mean
productivity. However, a comparison using fine-scale data (10 x 10 metre) shows that
sub-grid variance at large scales (1000 x 1000 metre) can be very large, particularly for
soil variables that can change abruptly over short distances.
Just as spatial input variables vary in quality and scale so may the temporal
(particularly weather) variables supplied to models. Using rainfall representation in
models as an example, an analysis of the intrinsic input data variability allows a more
sophisticated consideration of site selection that includes risk evaluation as a criteria.
Furthermore, it was shown that incorporating the distributional characteristics of
rainfall markedly affects the prediction of the risk associated with a particular
plantation development.

Item Type: Thesis - PhD
Authors/Creators:Mummery, Daryl Clarke
Copyright Holders: The Author
Copyright Information:

Copyright 2009 the author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

No access or viewing until 10 November 2011. After that date, available for use in the Library and copying in accordance with the Copyright Act 1968, as amended. Thesis (PhD)--University of Tasmania, 2009. Includes bibliographical references

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