A Bark Thickness Model for White Spruce in Alaska Northern Forests

Here we developed a simple linear model to estimate white spruce bark thickness in the northern forests of Alaska. Data were collected from six areas throughout interior and southcentral Alaska. Geographic variation of bark thickness was tested between the Alaska statewide model and for each geographic area. The results show that the Alaska statewide model is accurate, simple, and robust, and has no practical geographic variation over the six areas. The model provides accurate estimates of the bark thickness for white spruce trees in Alaska for a wide array of future studies, and it is in demand by landowners and forest managers to support their management decisions.We are obligated to Carol E. Lewis and Edmond C. Packee for supporting this bark thickness research. This research was also supported in part by the United States Department of Agriculture, McIntire-Stennis Act Fund ALK-03-12, and by the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks.We thank the associate editor, Han Chen, and an anonymous reviewer for their helpful comments

Circular 53

The regeneration of interior Alaska’s commercial forest lands is mandated by Alaska’s Forest Resources and Practices Act (1979). This act requires that regeneration be established adequate to ensure a sustained yield on forested lands from which the timber has been harvested. Post-logging regeneration efforts now are aimed at exposing mineral soil for the natural seeding of white spruce. Soil exposure has been accomplished by blade scarifying with a crawler tractor which provides large seed sites or by using a Bracke-type patch scarifier which produces small seed sites of about 2 ft2. Arlidge (1967) reports that larger seedbeds have greater regeneration success than smaller ones. Some researchers have found that the regeneration of the larger plots may be too successful, requiring weeding and precommercial thinning to bring stocking to satisfactory levels (Zasada and Grigal 1978). The Alaska Division of Forestry (DOF) has not been satisfied with the cost or effectiveness of either of these site-preparation practices.Introduction -- Methods -- Results and Discussion: Contractor #2, Contractor #3 -- Comparisons -- Conclusions -- Resources Cite

MP 2012-01

In 1994 the University of Alaska Fairbanks, School of Natural Resources and Agricultural Sciences, Agricultural and Forestry Experiment Station began a project to establish permanent sample plots (PSP) throughout the forests of northern and southcentral Alaska. Objectives of the project are to establish and maintain a system of PSPs to monitor forest growth, yield, forest health, and ecological conditions/change (Malone et al., 2009). To date, 603 PSPs have been established on 201 sites throughout interior and southcentral Alaska. The PSPs are square and 0.1 acre in size and in clusters of three. PSPs are remeasured at a five-year interval. The number of plot remeasurements after establishment ranges from one to three times. A large amount of data is collected at each site at time of establishment and at subsequent remeasurements. Four databases contain all the data: tree measurement and characteristics, site description, regeneration, and vegetation data. Vegetation data collected on the 0.1 acre PSPs includes species (trees shrub, herb, grass, and non-vascular plants) and cover, an estimate of the amount of the plot covered by the crown of each species (cover class) (Daubenmire, 1959). The vegetation database can be used by land managers and researchers to study species diversity and forest succession in addition to long-term monitoring of forest health. The species listed in Appendix 1 and in the vegetation database are presented by categories: tree, shrub, herb, grass, rush, sedge, fern, club moss, lichen, moss, and liverwort

Total and Merchantable Volume of White Spruce in Alaska

White spruce (Picea glauca [Moench] Voss) is a valuable commercial species found in interior and southcentral Alaska. Numerous regional and local volume tables or equations exist; however, no statewide model exists or has been tested for accuracy. There is a demand for an accurate model to determine the cubic-foot volume of white spruce trees in Alaska. Multiple models were developed for white spruce to estimate total and merchantable cubic-foot volume to a 2-, 4-, and 6-in. top. These multiple-entry (diameter and height) models were developed for both inside and outside bark volume from a 6-in. stump. The models were tested on a regional basis at various geographic locations and were shown to be highly accurate. The Alaska models chosen have R2 at or near 0.99 and mean square error from 0 to 0.16 for all models. These models are shown to be superior to other white spruce models in Alaska.This research was supported in part by the US Department of Agriculture, McIntire-Stennis Act Fund ALK-03-12, and by the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks

A PATH TOWARD IMPROVED MANAGEMENT OF THE NORTHERN FORESTS OF ALASKA: FOREST INVENTORY, BARK THICKNESS, AND STEM VOLUME

Thesis contributed as part of Tom Malone collection.This thesis provides three essential forest management tools that resource managers and researchers can use to improve management the northern forests of Alaska. The Cooperative Alaska Forest Inventory (CAFI) is a comprehensive database of northern forest conditions and dynamics. The basis for the CAFI database is a system of permanent sample plots located throughout interior and south-central Alaska. This information can be used to develop forest growth models and track long-term forest changes. The bark thickness model was developed because there was no published white spruce bark thickness model for Alaska. The data used to develop this and volume models were taken from stands located throughout interior and south-central Alaska. Analysis shows that this Alaska statewide bark thickness model accuracy estimates white spruce bark thickness when compared to other bark thickness models. Cubic-foot volume models were developed to estimate total stem and merchantable volume of white spruce in Alaska. These multiple-entry (diameter and height) models estimate volume both outside and inside bark. Analysis shows that these volume models were more accurate for Alaska when compared to published and unpublished white spruce models. These models can be used to estimate individual stem volume, volume per unit area, and to develop biomass models

A study of interdiurnal pressure and temperature variations in the free atmosphere over North America

The three-dimensional distribution of pressure and its variation with time are intimately associated with the principal weather phenomena. However, this association is indirect rather than direct and the link connecting the two is the horizontal and vertical field of motion. The relationship between the field of motion and the variation in time and space of pressure is one of interdependence. On the other hand, the field of motion seems causally related to most weather phenomena. Neither of these relationships is fully understood so it is not surprising that current attempts to utilize pressure distributions as tools for predicting weather phenomena do not meet with complete success. In this connection, the variation of pressure with time is of prime interest, inasmuch as it reflects physical processes currently in operation in the atmosphere and as it presages future developments. Study of these processes through an analysis of pressure variations is complicated by the compressibility of the atmosphere and by the observed fact that mass variations of either sign may be taking place within any layer in the atmosphere quite independently of variations of the mass within any other layer. Since the instantaneous distribution of pressure and its variation with time are hydrostatically related to the temperature field and its changes, it is advantageous to study the two elements simultaneously. Any consideration of the temperature field in the atmosphere inevitably requires that some attention must be devoted to the tropopause as a major first order discontinuity of temperature. Moreover, the significance of this general reglon of the atmosphere as a location of processes important in determining tropospheric pressure variations has been stressed by the Austrian School of Meteorologists. The problem of achieving a better understanding of the nature of pressure changes may be attacked in one or more of three different, though complementary ways - theoretically, descriptively, and/or, statistically. The theory has been discussed by Stüve, Defant, V. Bjerknes, J. Bjerknes, Solberg, and Bergeron, and more recently by Wulf and Obloy, J. Bjerknes and Holmboe, and Petterssen, as well as by others. The descriptive approach by means of detailed analyses of selected situations has been demonstrated in many investigations all over the world, among which are several particularly interesting studies in North America and Europe. The statistical treatment was first attempted by Dines and Schedler and considerably extended and refined by Haurwitz and others and by Penner. The statistical studies mentioned above have been based upon an analysis of mean conditions and mean changes in vertical columns of air assumed to be in hydrostatic equiiibrium. Since the problem is essentially dynamic, such analyses can never present the complete picture of the physical processes involved in pressure changes. However, in view of the present unsatisfactory state of knowledge concerning these processes, such statistical studies play an extremely valuable role in improving the general understanding of the complex systems of mass variations which are integrated into pressure changes by the atmosphere. They present mean conditions against which descriptive and theoretical studies may be evaluated with regard to representativeness and applicability, respectively. Furthermore, they are systematic summaries of actual conditions which may properly serve as a guide to the direction in which further theoretical and descrptive investigations may most profitably proceed. The paucity of upper air data has restricted the scope of previous studies along these lines and so it is the aim of this investigation to utilize the information recently available as a result of the well organized network of radiosonde observation stations in North America to extend these studies. In particular, it is desired to investigate the geographical and seasonal differences in the mean values of pressure and temperature variation at all levels and the related upper air conditions. It is hoped that the greater number of observations in the stratosphere may throw some light on its true importance with respect to tropospheric pressure variations and that mean conditions throughout the lower atmosphere may be so defined that it will be possible to set up certain requirements which any proposed mechanism for pressure changes must satisfy in order to be thoroughly consistent with reality. The observational material and the methods of analysis will be discussed in Chapter I. Chapters II, III, and IV will be devoted to a description of the results of the statistical analysis. Since the interpretations of one aspect of the study depend upon the results of some of the other aspects, most of the interpretations and conclusions wil be discussed in Chapter V

Cowboys or Commanders: Does Information Technology Lead to Decentralization?

The model distinguishes between two kinds of decentralization: connected and unconnected. Our model predicts that unconnected (i.e., independent) decentralized decision makers should be common when communication costs are high. Then, as communication costs fall, centralized decision makers should become more desirable. Finally, as communication costs fall still further, connected decentralized decision makers should become desirable in many situations

Making Business Predictions by Combining Human and Machine Intelligence in Prediction Markets

Computers can use vast amounts of data to make predictions that are often more accurate than those by human experts. Yet, humans are more adept at processing unstructured information and at recognizing unusual circumstances and their consequences. Can we combine predictions from humans and machines to get predictions that are better than either could do alone? We used prediction markets to combine predictions from groups of people and artificial intelligence agents. We found that the combined predictions were both more accurate and more robust than those made by groups of only people or only machines. This combined approach may be especially useful in situations where patterns are difficult to discern, where data are difficult to codify, or where sudden changes occur unexpectedly

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance