Identification of key soil indicators influencing plantation productivity and sustainability across a national trial series in New Zealand

Watt, Michael S.; Davis, M. R.; Clinton, Peter W.; Coker, Graham; Ross, C. W.; Dando, John; Parfitt, R. L.; Simcock, Robyn

doi:10.1016/j.foreco.2008.04.024

Cited by 37 publications

(33 citation statements)

References 20 publications

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“…Temperaturerelated variables have generally been found to have the greatest influence on P. radiata productivity within New Zealand (Watt et al 2005;Watt et al 2008;Jackson and Gifford 1974;Hunter and Gibson 1984;Watt et al 2010) and were found here to be important determinants of both the 300 Index (summer degree frost days) and Site Index (max temperature in summer). The positive relationship often found between air temperature and tree growth is thought to be principally driven by the lengthening of the growing season (Lieth 1973;Kerkhoff et al 2005).…”

Section: Discussionmentioning

confidence: 57%

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

Kimberley

Watt

Harrison

2017

N.Z. j. of For. Sci.

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Background: Two indices, the 300 Index and Site Index, are commonly used to quantify productivity of Pinus radiata D.Don within New Zealand. Although maps of these indices exist, availability of new data and modifications to underlying models makes a refit of these prediction surfaces desirable. Prediction errors of such surfaces have only been reported at a plot-level scale, but their application is invariably at a larger scale where prediction accuracy should be better. The objectives of this study were to: (i) develop updated predictive surfaces for the 300 Index and Site Index; and (ii) characterise the relationship between prediction error and spatial scale for both surfaces. Methods: Models were developed using a dataset of 4108 permanent sample plots from throughout New Zealand. Productivity indices were estimated from plot measurements and environmental variables extracted for each plot. Data were randomly split into fitting and validation datasets and surfaces developed from the fitting dataset for the 300 Index and Site Index using partial least squares regression, ordinary kriging and regression kriging. Prediction accuracy across a range of scales from 0.2 to 200 km was evaluated using the validation dataset. Results: Regression kriging was found to be the most accurate method for describing spatial variation in the 300 Index and Site Index across New Zealand. Examination of changes in prediction error with spatial scale demonstrated a gradual decline in error from the plot level with increasing scale. Conclusions: This study provides accurate maps of both the 300 Index and Site Index across New Zealand. Analysis of the effects of scale on prediction accuracy indicates that 95% confidence intervals of predictions for the 300 Index based on these maps averaged over an area of about 700 ha are half those of plot-level predictions and halve again at a scale of about 20,000 ha. For the Site Index, the improvement in precision with increasing scale is more gradual with 95% confidence intervals halving at a scale of about 20,000 ha and halving again at a scale of about 250,000 ha.

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Section: Discussionmentioning

confidence: 57%

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

Kimberley

Watt

Harrison

2017

N.Z. j. of For. Sci.

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“…Multiple linear regression (MLR) techniques, as noted earlier, have been used previously to model P. radiata productivity for New Zealand (e.g., Watt et al, 2005Watt et al, , 2008. Evaluating long range variation can be undertaken using global regression (Burrough and McDonnell, 1998) and the PLS regression technique used here explores the relationship between spatially variable attributes.…”

Section: Discussionmentioning

confidence: 99%

“…Multiple linear regression techniques have been used in a number of studies to determine the major environmental contributors of New Zealand P. radiata productivity and site quality (Jackson and Gifford, 1974;Hunter and Gibson, 1984;Woollons et al, 2002;Watt et al, 2005Watt et al, , 2008). An early map was developed by Eyles (1986) giving P. radiata Site Index by associating it with New Zealand land use capability units (NWASCA, 1979) in conjunction with expert knowledge.…”

Section: Introductionmentioning

confidence: 99%

Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand

Palmer

Höck

Kimberley

et al. 2009

Forest Ecology and Management

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“…The Bray2P concentrations (48 mg kg À1 for 0-150 mm, for the first extraction; Table 5) were well above the 12 mg kg À1 threshold for radiata pine (Skinner et al, 1991;Palmer et al, 2005). Total soil P concentrations in 0-150 mm averaged 683 mg kg À1 for 1991 and 1996 (Scott, 2002), which also would be considered adequate for radiata pine (Watt et al, 2005(Watt et al, , 2008.…”

Section: General Soil Fertilitymentioning

confidence: 91%

“…The soil total N concentration in 1991 and 1996 (0.26 and 0.22%, respectively, in the top 150 mm; Table 5) is well below the optimum of 0.5% for radiata pine but this was partly offset by the low C:N ratio of 10:1, which makes the soil N more readily available (Jackson and Gifford, 1974;Watt et al, 2005Watt et al, , 2008. The soil N concentrations are also rated low for growing most other plants (Blakemore et al, 1987).…”

Section: General Soil Fertilitymentioning

confidence: 99%

Using vector analysis to understand temporal changes in understorey-tree competition in agroforestry systems

Mead¹,

Scott²,

Chang³

2010

Forest Ecology and Management

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Identification of key soil indicators influencing plantation productivity and sustainability across a national trial series in New Zealand

Cited by 37 publications

References 20 publications

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand

Using vector analysis to understand temporal changes in understorey-tree competition in agroforestry systems

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