Thesis (Ph.D.)--University of Melbourne, Dept. of Zoology, 1978. / Typescript (photocopy). Includes bibliographical references.

Thesis (Ph. D.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (p. 172-194). Also available via the World Wide Web.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on June 5, 2009. "May 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Forestry Sciences." "Presented to the faculty of California Polytechnic State University, San Luis Obispo." Major professor: Walter R. Mark, Ph.D. Includes bibliographical references (p. 39-43). Also available on microfiche.

Thesis (Ph. D.)--Oregon State University, 1994. / Typescript (photocopy). Includes bibliographical references. Also available on the World Wide Web.

Thesis (Ph. D.)--University of Waikato, 2007. / Title from PDF title page (viewed on May 6, 2007). Includes bibliographical references.

Memoria para optar al Título Profesional de Ingeniero Forestal / Se midió mensualmente el índice de área foliar en plantaciones de Pinus radiata D. Don en la región del Bío Bío en las cuatro principales zonas de crecimiento para esta especie (IV, V, VI y VII) durante 24 meses entre Octubre de 2010 y Octubre de 2012. Los predios donde se ubicaron los ensayos fueron San Pedro de la Paz (predio Lomas Coloradas), Nacimiento (predio Nacimiento), Santa Bárbara (predio Santa Bárbara) y Cabrero (predio Tapihue). Para cada sitio se consideraron tres clases de edad ([0-7), [7-14) y [14+)). En cada uno de los 12 puntos de Zona-Edad, se procedió a instalar un conglomerado de 3 parcelas permanentes de 1.000 m2, totalizando 36 parcelas, entre los meses de Agosto y Septiembre de 2010. Estos predios y rodales se ubican a menos de 10 km de las estaciones meteorológicas de Escuadrón (Lomas Coloradas), Pichún (Nacimiento), Santa Bárbara (Santa Bárbara) y Trilahue (Tapihue) que mantiene Forestal Mininco S.A. La edad se correlacionó positivamente con el índice de área foliar (L) como era de esperar. Además se aprecia que el L crece fuertemente desde la clase de edad [0-7) a la [7-14), comparado con el crecimiento entre la clase de edad [7-14) a [14+). Lo anterior implica que el mayor crecimiento en área foliar se genera antes de los 14 años. Además a lo largo de los 24 meses se observa que el L aumenta fuertemente en la clase de edad [0-7), en menor cuantía en la clase de edad [7-14), y muestra un crecimiento prácticamente imperceptible en la clase [14+) años.

Memoria para optar al Título Profesional de Ingeniero Forestal / Se realizaron inventarios forestales mensuales y mediciones fisiológicas trimestrales en plantaciones de Pinus radiata D. Don en la Región del Bío Bío en las cuatro principales zonas de crecimiento para esta especie (IV, V, VI y VII) durante 24 meses entre Agosto de 2010 y Agosto de 2012. Los predios donde se ubicaron los ensayos fueron Lomas Coloradas en la Comuna de San Pedro de la Paz, Nacimiento en la Comuna de Nacimiento, Santa Bárbara en la Comuna de Santa Bárbara y Tapihue en la Comuna de Cabrero. Para cada sitio se consideraron tres clases de edad (0-7, 7-14 y 14+ años). En cada uno de las 12 instancias de Zona-Edad, se procedió a instalar un conglomerado de 3 parcelas permanentes de 1.000 m2, totalizando 36 parcelas. Estos predios y rodales se ubicaron a menos de 10 km de las estaciones meteorológicas de Escuadrón (Lomas Coloradas), Pichún (Nacimiento), Santa Bárbara (Santa Bárbara) y Trilahue (Tapihue), pertenecientes a Forestal Mininco S.A.

Background: The objective of this study was to verify the effect that plot locational errors, termed plot co-registration errors, have on the strength of regression between LiDAR canopy metrics and the measured total standing volume (TSV) of plots in a Pinus radiata forest. Methods: A 737 hectare plantation of mature Pinus radiata located in Northern Hawkes Bay was selected for the study. This forest had been measured in a pre-harvest inventory and had aerial LiDAR assessment. The location of plots was verified using a survey-grade GPS. Least square linear regression models were developed to predict TSV from LiDAR canopy metrics for a sample of 204 plots. The regression strength, accuracy and bias was compared for models developed using either the actual (verified) or the incorrect (intended) locations for these plots. The change to the LiDAR canopy metrics after the plot co-registration errors was also established. Results: The plot co-registration error in the sample ranged from 0.7 m to 70.3 m, with an average linear spatial error of 10.6 m. The plot co-registration errors substantially reduced the strength of regression between LiDAR canopy metrics and TSV, as the model developed from the actual plot locations had an R2 of 44%, while the model developed from the incorrect plot locations had an R2 of 19%. The greatest reductions in model strength occurred when there was less than a 60% overlap between the plots defined by correct and incorrect locations. Higher plot co-registration errors also caused significant changes to the height and density LiDAR canopy metrics that were used in the regression models. The lower percentile elevation LiDAR metrics were more sensitive to plot co- registration errors, compared to higher percentile metrics. Conclusion: Plot co-registration errors have a significant effect on the strength of regressions formed between TSV and LiDAR canopy metrics. This indicates that accurate measurements of plot locations are necessary to fully utilise LiDAR for inventory purposes in forests of Pinus radiata.

Monterey pine (Pinus radiata), a native tree to California and two Mexican islands, is important both ecologically and economically. Outside native stands, Monterey pines are grown for landscaping in California and on plantations around the world. Pitch canker, a disease caused by the fungus Gibberella circinata Nirenberg & O’Donnell (Fusarium circinatum Nirenberg and O'Donnell) is threatening the survival of Monterey pines. The disease currently affects Monterey pines in many parts of the world including the native stands. No effective chemical or biological control is available but some Monterey pines show resistance to the disease. The purpose of this project was to develop a working protocol for producing genetic clones of the resistant pines through micropropagation. These genetic clones will be used for outplanting in places outside the native stands for ornamental and plantation purposes. This project analyzes the results of ten trials with varied parameters and bases the final protocol on the parameters used in the trial that induces the growth of new shoots. The final protocol developed in this project describes, step-by-step, the media preparation for the initiation, plant material collection, surface sterilization of plant material, plating in media and initiation of shoots on explants. The protocol calls for collecting shoot tips with hardened buds that have not yet elongated, then washing the shoot tips in sterile water with Tween 20 for 15 minutes. The shoots tips are then surface sterilized in a 50% bleach solution for 20 minutes. The explants are broken into disks (to minimize damage to the cells) by inserting the tip of a scalpel and tilting it slightly. The initiation media shown to induce growth consists of ½ strength LePoivre basal salt mixture, 5mg/L benzylaminopurine, 3% sucrose and 0.8% agar and is adjusted to a pH of 5.7, then autoclaved for 20 minutes. The explants are inserted into solidified media and incubated in a growth chamber programmed for 16 hours of light and 8 hours of dark with temperatures of 27ºC and 22ºC and light irradiance of 80µEm-2s-1. After 1 month the protocol calls for transferring the growing shoots to elongation media with full LP basal salts and transferring every month. When the number of desired shoots has been reached the forthcoming protocol for rooting can be followed.

The New Zealand forest industry produces a diverse range of log grades and sorts to meet domestic and export market demands and to maximise returns to the forest grower. An implication for the supply chain is the number of log grades and sorts a harvesting operation is expected to produce from one species, radiata pine (Pinus radiata). The number of log grades and sorts can impact on landing size and layout requirements, value recovery, log-making complexity, machine utilisation and quality control requirements. A study was conducted to investigate if the number of log sorts affects mechanised log processing productivity and value recovery. This would determine if any gross value gains derived from producing a higher number of sorts are offset by losses in log processing productivity. Two landing-based mechanised log processors at cable yarder harvesting operations were studied using different cutting scenarios producing five, nine, twelve and fifteen log sorts. The study collected data from over 26 hours of mechanised processing which included the processing of 578 stems at an average piece size of approximately 1.6 m³. Machine utilisation results showed processors spending 84% of total time on productive tasks and that 49% of total time was spent on the primary productive tasks of log processing. Quadratic regressions were used to model log processing productivity trends which showed piece size and cutting scenario as significant predictor variables (p-value <0.01). There was a significant difference between cutting scenario with five log sorts and the cutting scenarios with twelve and fifteen log sorts (p-values <0.05), as well as a significant difference between the nine and fifteen log sort cutting scenarios (p-value <0.01). There was not enough evidence to suggest productivity was different between cutting scenarios producing five and nine log sorts. Based on this analysis, it was likely that the null hypothesis that the number of log sorts does not affect log processing productivity should be rejected. At a piece size of 2 m³, the productivity model estimated processing productivity was 10% higher producing nine log sorts compared to producing fifteen log sorts. A linear regression model showed a strong relationship between gross value recovery, piece size and cutting scenario (p-value <0.01). Gross value recovery increased as the number of log sorts increased. A significant model suggested it is likely null hypothesis 2, that the number of log sorts does not affect gross value recovery, should be rejected. There were only some differences in variances between cutting scenarios which were statistically significant. Both the average results and regression estimates showed the five log sort cutting scenario recovering 94% of the value of the cutting scenario with fifteen log sorts. Incremental gains in value recovery as the number of log sorts increased were marginal, which appeared to be due to log prices for many major log grades trading in a close range in relation to historic price trends. Regression trends for productivity and gross value recovery indicated that the most optimal cutting scenario, in terms of processing value outturn per productive machine hour, was the cutting scenario producing nine log sorts. This suggests that declines in processor productivity offset gains in gross value recovery when producing twelve and fifteen log sorts. Market sensitivity analysis suggested that differentials in log prices impact on the number of log sorts which optimise the value outturn per productive machine hour from log processing.