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Towards national-scale risk assessment of plantation forest soils in New Zealand: phosphorus nutrient depletion

David J. Palmer 1, David J. Lowe1, Barbara Höck2, Peter W. Clinton2, Mark Kimberley2, Tim W. Payn2 and Gabrielle T. Palmer1

1Department of Earth Sciences, University of Waikato, Private Bag 3105, Hamilton, New Zealand. www.erth.waikato.ac.nz
Email djpalmer@waikato.ac.nz and d.lowe@waikato.ac.nz and gabip@waikato.ac.nz
2
Forest Research, Private Bag 3020, Rotorua, New Zealand. www.forestresearch.co.nz Email peter.clinton@forestresearch.co.nz and barbara.hock@forestresearch.co.nz and mark.kimberley@forestresearch.co.nz and tim.payn@forestresearch.co.nz

Abstract

Over the past decade New Zealand has implemented new environmental policies and legislation to ensure that natural resources are used sustainably and, from overseas market perspectives especially, in an environmentally friendly way. These policies have driven the forestry sector to undertake wide-ranging new research including the development of long-term nutrient budget models to try to determine how many successive rotations forestry plantations can sustain productivity without deleterious nutrient depletion. In this paper we focus on modelling soil phosphorus nutrient pools over multiple rotations of Pinus radiata at a national scale for North Island and South Island. An existing Forest Research nutrient supply model will be used to predict pool and flux parameters. The model will use GIS and computer scripts to undertake sensitivity analysis of the outcomes resulting from changes in model parameters. Geostatistical techniques will be used to spatially interpolate Pinus radiata productivity data normalised using the Forest Research-developed mean annual increment 300 index (MAI 300 dataset). Various regression modelling and stratification techniques will be explored utilising existing and newly-derived spatial datasets to predict other model parameters. Study outcomes will provide a series of maps at a national scale (1:50 000) delineating areas at risk of P depletion. The model design will facilitate data upgrades as new and improved data become available.

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