Campaign and site effects in LiDAR prediction models for site-quality assessment of radiata pine plantations in South Australia

2010 ◽  
Vol 31 (5) ◽  
pp. 1155-1173 ◽  
Author(s):  
J. Rombouts ◽  
I. S. Ferguson ◽  
J. W. Leech
Keyword(s):  
Quality Assessment ◽  
Site Effects ◽  
Prediction Models ◽  
South Australia ◽  
Radiata Pine ◽  
Pine Plantations ◽  
Site Quality

Bracken fern control for radiata pine plantations in South Australia

1989 ◽  
Vol 52 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Ulla Karjalainen ◽  
David Boomsma
Keyword(s):  
South Australia ◽  
Radiata Pine ◽  
Pine Plantations ◽  
Bracken Fern

Boron nutrition of radiata pine plantations in Australia

1997 ◽  
pp. 83-88 ◽  
Author(s):  
Marcia J. Lambert ◽  
John Turner ◽  
Jim Knott
Keyword(s):  
Radiata Pine ◽  
Pine Plantations

Fertilized Midrotation-Aged Slash Pine Plantations—Stand Structure and Yield Prediction Models

1989 ◽  
Vol 13 (2) ◽  
pp. 76-80 ◽  
Author(s):  
Robert L. Bailey ◽  
Thomas M. Burgan ◽  
Eric J. Jokela
Keyword(s):  
Prediction Models ◽  
Stand Structure ◽  
Basal Area ◽  
Slash Pine ◽  
Pine Plantations ◽  
Individual Tree ◽  
Investment Opportunities ◽  
User Friendly ◽  
Volume Equations ◽  
Soil Groups

Abstract Data from 263 plots in a regional fertilization study of midrotation-aged slash pine plantations were used to fit prediction equations for basal area, trees per acre, stand average dominant height, diameter distributions, and individual tree heights. The equations include N and P fertilizationrates and CRIFF soil groups as predictor variables. The survival model also accounts for the accelerating effect of fusiform rust on mortality rate. Using published tree volume equations, the prediction of volumes by dbh class for fertilized slash pine plantations is now possible. This integratedsystem of equations is available as a user-friendly computer program that can calculate expected yields by diameter class and aid the forester in evaluating investment opportunities that include forest fertilization. South. J. Appl. For. 13(2):76-80.

A Model to Generate Stand Strategies for Intensively Managed Radiata Pine Plantations

1977 ◽  
Vol 40 (4) ◽  
pp. 255-267 ◽  
Author(s):  
B. J. TURNER ◽  
R. W. BEDNARZ ◽  
J. B. DARGAVEL
Keyword(s):  
Radiata Pine ◽  
Pine Plantations ◽  
Intensively Managed

scholarly journals Factors influencing the growth of radiata pine plantations in Chile

2012 ◽  
Vol 86 (1) ◽  
pp. 13-26 ◽  
Author(s):  
J. Alvarez ◽  
H. L. Allen ◽  
T. J. Albaugh ◽  
J. L. Stape ◽  
B. P. Bullock ◽  
...  
Keyword(s):  
Radiata Pine ◽  
Pine Plantations ◽  
Factors Influencing

The role of permanent site factors in the assessment of soil treatment effects: A case study with a site preparation trial in jack pine plantations on glacial outwashes

2009 ◽  
Vol 89 (1) ◽  
pp. 81-91 ◽  
Author(s):  
F. Marquis ◽  
D. Paré
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Tree Growth ◽  
Soil Texture ◽  
Quality Index ◽  
Jack Pine ◽  
Site Preparation ◽  
Pine Plantations ◽  
Site Quality

In silvicultural trials, the confounding influence of permanent soil properties is assumed to be minimal. A covariance analysis using total elemental concentrations of parent material (geochemistry) and soil particle size distribution (texture) was used to understand the role that these variables could play in tree growth and foliar nutrient status, and in the growth response to site preparation of 16- to18-yr-old jack pine plantations growing on seemingly homogeneous glacial outwash deposits. Three treatments were tested in a replicated design: direct plantation, and site preparation with TTS or with Bräcke. The combination of particle size distribution and soil geochemistry explained the site quality index (SQI) in control plots (R2 = 0.94) better than soil texture alone. In all plots, SQI was strongly related to K and Mg concentrations in foliage and in humus and, to a lesser degree, to foliar N concentrations. A covariance analysis using soil texture and geochemical composition indicated that while site preparation had an effect on tree growth, the sites that responded the most to site preparation were the ones with the lowest growth potential as determined by soil texture and geochemistry. Key words: Silviculture, site preparation, site quality index, growth, permanent soil factors, texture, geochemistry, nutrition

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