One of the key factors in the site classification of the biogeoclimatic ecosystem classification is soil nutrient regime. Soil nutrient regime (SNR) represents the amount of essential soil nutrients available to plants over a period of several years. SNRs classes are assessed based on field identifiable (qualitative) criteria, not using quantitative measures. There have been several studies that attempted to quantitatively characterize regional soil nutrient gradients in the Coastal Western Hemlock (CWH) zone. In the study summarized here, the soils are influenced by a perhumid cool mesothermal climate. The objective of the study was to examine relationships between soil chemical properties and field-estimated SNRs.

Site classification of the biogeoclimatic ecosystem classification system is based on climatic regime (expressed by biogeoclimatic subzone), soil moisture regime (SMR), and soil nutrient regime (SNR). A SNR represents a segment of a regional soil nutrient gradient, i.e., a population of soils which provide similar levels of plant-available nutrients over a long period. SNR is identified in the field using a number of easily observable soil morphological properties and indicator plant species. However, we do not know the extent to which soil nutrient properties are supported by these indirect field-estimates. There have been several studies that quantitatively characterized regional soil nutrient gradients in different climatic regions (see Sciencia Silvica Number 21 for subalpine coastal forests), but this has not been done in the subalpine interior forest (Engelmann Spruce - Subalpine Fir (ESSF) zone) where soils are influenced by a continental subalpine boreal climate. In the study summarized here, relationships between soil chemical properties and field-estimated SNRs are examined and soil chemical properties and field-identified SNRs are related to the site index of subalpine fir (Abies lasiocarpa (Dougl. ex Loud.) Forbes) and Engelmann spruce (Picea engelmannii Parry ex Engelmann) - two major timber crop species in the ESSF zone.

Knowledge of ecological characteristics of sites and growth of trees on different sites is fundamental for silvicultural decision-making and planning. With the biogeoclimatic ecosystem classification in place in British Columbia, silvicultural management has been given an ecological foundation; however, relationships between growth and site quality have not yet been fully investigated, particularly for high-elevation tree species and sites. One of the contributing factors for this situation is limited knowledge of forest productivity in the high-elevation Mountain Hemlock (MH) and Engelmann Spruce - Subalpine Fir (ESSF) biogeoclimatic zones. Consequently, the management and planning in the high-elevation forest is fraught with difficulties and uncertainties. Current harvest rates of old-growth forest stands and the method and distribution of cuttings in these zones suggest that there needs to be more recognition of the uppermost elevation limit for harvesting. Subalpine fir (Bl), Engelmann spruce (Se), and lodgepole pine (Pl) are important timber crop species in the interior high-elevation forest which is represented predominantly by the subalpine boreal ESSF zone. This zone extends from 49° to approximately 57° N latitude and from approximately 900 to 1,700 m in the north, from 1,200 to 2,100 m in central BC, and from 1,500 to 2,300 m in the south. In view of this relatively wide climatic and edaphic amplitude, a large variability in productivity is expected. The objective of this study was to quantify relationships between site index (height @ 50 yrs @ bh) of Bl, Se, and Pl, and three ecological determinants of site quality: climate, soil moisture, and soil nutrients. Quantitative relationships between site index and these measures provide predictive models for estimating site index. Additionally, we compared the site indices of the three study species to each other to examine their early height growth performance on the same sites.

Site classification in the biogeoclimatic ecosystem classification system is based on three differentiating properties: climatic regimes (expressed by biogeoclimatic subzones or variants), soil moisture regimes (SMRs), and soil nutrient regimes (SNRs). A SNR represents a segment of a regional soil nutrient gradient, i.e., soils which provide similar levels of plant-available nutrients over a long period. SNRs are identified in the field using a number of easily observable soil morphological properties and indicator plant species. However, we need to know to what extent soil nutrient properties support these indirect field-estimates. There have been several studies that quantitatively characterize regional soil nutrient gradients in different climatic regions, but no study has yet been done in the subalpine coastal forest (Mountain Hemlock zone). Influenced by a maritime subalpine boreal climate, high-elevation coastal soils differ from low-elevation soils by having a thicker forest floor and a higher organic matter content. In the study summarized here, relationships between soil chemical properties and field-estimated SNRs are examined and soil chemical properties and field-identified SNRs are related to the site index of Pacific silver fir (Abies amabilis (Dougl. ex Loud.) Forbes) - one of the major timber crop species in the Coastal Western Hemlock and Mountain Hemlock zones.

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The objective of the dissertation is confrontation of influence of different methodics on the financial health of a company on ranking of a company. Selected models of financial health classify the file of 149 agricultural companies. Particular task is to determine 5 agricultural companies with the best indicator of financial health and 5 companies with the worst indicator. At this chosen file of five best and five worst companies is subsequently evaluated rate of weighted factors on the overall value of financial health. At the same time the influence of one and two indicators on the overall financial health of a company is analysed. Ascertained results are processed/compiled in the form of charts and are verbally commented.

Intensive site preparation practices and their effect on nitrogen cycling have been implicated as possible causes of productivity declines on forest sites in Australia and New Zealand. This study was initiated in order to determine the effects of site preparation intensity upon N distribution and availability in loblolly pine (Pinus taeda L.) plantations in Virginia. In the summer of 1982, three forest sites at the Reynolds Homestead Research Center on the Virginia Piedmont were clearcut. In the fall of the same year all three sites were prepared for planting using one of the following treatments: 1. shear, rake, disk (S,R,D) (3-passes); 2. shear-disk (S-D) (1-pass); and 3. chop, burn (C,B) (high intensity burn). During March of 1983, 1-0 genetically improved loblolly pine seedlings were planted on all sites. Pine biomass was greatest on the S,R,D area after three growing seasons. Total biomass and N content (NCONT) of native vegetation and forest floor were greatest in the S-D area. Total N in the upper 15 cm of mineral soil was also greatest in the S-D area. Total system N was highest in the S-D area and this treatment is more N-conservative than either of the more intensive treatments. During the third growing season potentially mineralizable N levels were highest on the two disked treatment areas, 157 and 144 kg N/ha for the s-o, and S,R,D areas, respectively. Pine foliar nutrient concentrations determined after the second and third growing seasons provided no evidence of existing or impending nutrient deficiencies. These results show that short-term pine nutrition and growth were not adversely affected by reductions of N capital on these sites. However, if wasteful practices, such as raking and burning with high intensity fires, are also used to establish subsequent stands on these same sites, cumulative losses of N could result in productivity declines. / M.S.

In the past 15 years, a large amount of public-domain lidar data has been collected over the Southeastern United States. Most of these acquisitions were undertaken by government agencies, primarily for non-forestry purposes. That is, they were collected mostly to aid in the creation of digital terrain models and to support hydrological and engineering assessments. Such data is not ideal for forestry purposes mainly due to the low pulse density per square meter, the high scan angles and low swath overlaps associated with these acquisitions. Nevertheless, the large area of coverage involved motivated this work. In this dissertation, I first look at how such lidar data (from non-forestry acquisitions) can be combined with National Forest Inventory tree height data to generate a large-area canopy height model. A simple linear regression model was developed using two lidar-based metrics as predictors: the 85th percentile of heights of canopy first returns and the coefficient of variation of the heights of canopy first returns. This model had good predictive ability over 76 disparate lidar projects, covering an area of approximately 297,000 square kilometers between them. Factors leading to the residual lack-of-fit of the model were also analyzed and quantified. For example, predictive ability was found to be better for softwood forests, forests with more homogeneous vegetation structure and for terrains with gentler slopes. Given that as much as 30% of the US is covered by public domain non-forestry lidar acquisitions, this is a first step for constructing a national wall-to-wall vertical vegetation structure map, which can then be used to ask important questions regarding forest inventories, carbon sequestration, wildlife habitat suitability and fire risk mitigation. Then, I examined whether such lidar data could be further used to predict understory shrub presence over disparate forest types. The predictability of classification model was low (accuracy = 62%, kappa = 0.23). Canopy occlusion factors and the heterogeneity of the understory layer were implicated as the main reasons for this poor performance. An analysis of the metrics chosen by the modeling framework highlighted the importance of non-understory metrics (metrics related to canopy openness and topographic aspect) in influencing shrub presence. As the proposed set of metrics were developed over a wide range of temperate forest types and topographic conditions of Southeastern US, it is expected that it will be useful for more localized future studies. Lastly, I explored the possibility of combining lidar-derived canopy height maps with Landsat-derived stand-age maps to predict plantation pine site index over large areas (site index is a measure of forest productivity). The model performance was assessed using a Monte Carlo technique (RMSE = 3.8 meters, relative RMSE = 19%). A sample site index map for large areas of Virginia and South Carolina was generated (map coverage area: 832 sq. km) and implications were discussed. Analysis of the resulting map revealed the following: (1) there is an increase in site index in most areas, compared to the 1970s, and (2) approximately 83% of the area surveyed had low levels of productivity (defined as site index < 22.0 meters for base age of 25 years). This work highlights the efficacy of combining lidar-based canopy height maps with other similar remote sensing based datasets to understand aspects of forest productivity over large areas, and to help make policy-relevant recommendations. / Ph. D.

The aim of this study was to investigate if SI from HPR-data from harvesters and soil moisture classes from digital depth to water maps could be used to support site adaption of regenerations within stands. The study was made on pine and spruce stands in central Sweden. The number of plants/ha, plant height, growth and damage were measured at plot level and for these plots also soil moisture classes and SI were derived from digital maps and HPR. The study shows a potential using SI from HPR and depth to water maps for site adaption of regenerations and to vary tree species within stands. Variations of SI and soil moisture are important within stands regarding different tree species establishment, growth and damage. But also, for sites with medium SI were the choice between pine and spruce is not obvious and in stands with a high spread in SI.

Ecosystem-specific forest management requires comprehension of tree species productivity in managed settings, and how this productivity varies with the ecological determinants of site quality, i.e., the environmental factors that directly affect the growth of plants: light, heat, soil moisture, soil nutrients, and soil aeration. A good understanding of this variation is necessary for making species- and site-specific silvicultural decisions to maximize productivity. Productivity of a given species is usually measured by site index (tree height at 50 years at breast height age). Quantitative relationships between site index and these measures of site quality provide predictive models for estimating site index. Pacific silver fir (Abies amabilis (Dougl. ex Loud.) Forbes) is an important timber crop species in the coastal forests of British Columbia. In relation to climate, its range in southwestern British Columbia extends from sea level to almost timberline, and from the hypermaritime region on western Vancouver Island to the subcontinental region on the leeward side of the Coast Mountains. In relation to soils, its range extends from slightly dry to wet sites and from very poor to very rich sites. In view of this relatively wide climatic amplitude, a large variability in productivity can be expected. It is particularly important to consider the growth performance of Pacific silver fir when decisions are made regarding whether or not to cut stands on high-elevation sites. In the study summarized here, relationships between Pacific silver fir site index and selected ecological measures of site quality were examined, and site index models using these measures as predictors were developed.