The influence of weather variation on regional growth of Douglas fir stands in the U.S. Pacific Northwest

1990 ◽  
Vol 54 (1) ◽  
pp. 295-305 ◽  
Author(s):  
Charles E. Peterson ◽  
Linda S. Heath
Keyword(s):  
Pacific Northwest ◽  
Regional Growth ◽  
Douglas Fir ◽  
Weather Variation ◽  
The U.S

scholarly journals Incorporation of Genetic Gain into Growth Projections of Douglas-Fir Using ORGANON and the Forest Vegetation Simulator

2010 ◽  
Vol 25 (2) ◽  
pp. 55-61 ◽  
Author(s):  
Peter J. Gould ◽  
David D. Marshall
Keyword(s):  
Pacific Northwest ◽  
Genetic Gain ◽  
Growth Models ◽  
Regional Growth ◽  
Site Index ◽  
Douglas Fir ◽  
Forest Vegetation ◽  
Planting Density ◽  
Forest Vegetation Simulator ◽  
Stand Management

Abstract Growth models for coast Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) are generally based on measurements of stands that are genetically unimproved (or woods-run); therefore, they cannot be expected to accurately project the development of stands that originate from improved seedlots. In this report, we demonstrate how early expected gain and genetic-gain multipliers can be incorporated into growth projection, and we also summarize projected volume gains and other aspects of stand development under different levels of genetic gain, site productivity, and initial planting density. Representative tree lists that included three levels of productivity (site index = 100, 125, and 150 ft; base = 50 years)and three initial planting densities (302, 435, and 602 trees/ac) were projected from ages 10 to 60 years under three scenarios using two regional growth models (Stand Management Cooperative version of ORGANON and the Pacific Northwest variant of the Forest Vegetation Simulator). The two models projected similar percentage volume gains for improved seedlots. Seedlots with a genetic worth (GW) of 5% for height and diameter growth were projected to have volume gains of 3.3–5.8% over woods-run stands at 40 years and 2.1–3.2% at 60 years. Volume gains were projected to approximately double when GW was increased from 5 to 10%.

Erosional response to northward-propagating crustal thickening in the coastal ranges of the U.S. Pacific Northwest

2013 ◽  
Vol 313 (8) ◽  
pp. 790-806 ◽  
Author(s):  
G. Balco ◽  
N. Finnegan ◽  
A. Gendaszek ◽  
J. O. H. Stone ◽  
N. Thompson
Keyword(s):  
Pacific Northwest ◽  
Crustal Thickening ◽  
The U.S

scholarly journals Estimating a global demand model for soybean traffic through the Panama Canal

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Javier Ho ◽  
Paul Bernal
Keyword(s):  
Pacific Northwest ◽  
Least Square ◽  
Panama Canal ◽  
Demand Model ◽  
Department Of Agriculture ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Global Demand ◽  
Alternative Routes ◽  
The U.S

AbstractThis study attempts to fit a global demand model for soybean traffic through the Panama Canal using Ordinary Least Square. Most of the soybean cargo through the interoceanic waterway is loaded on the U.S. Gulf and East Coast ports -mainly destined to East Asia, especially China-, and represented about 34% of total Panama Canal grain traffic between fiscal years 2010–19. To estimate the global demand model for soybean traffic, we are considering explanatory variables such as effective toll rates through the Panama Canal, U.S. Gulf- Asia and U.S. Pacific Northwest- Asia freight rates, Baltic Dry Index, bunker costs, soybean export inspections from the U.S. Gulf and Pacific Northwest, U.S. Gulf soybean basis levels, Brazil’s soybean exports and average U.S. dollar index. As part of the research, we are pursuing the estimation of the toll rate elasticity of vessels transporting soybeans via the Panama Canal. Data come mostly from several U.S. Department of Agriculture sources, Brazil’s Secretariat of Foreign Trade (SECEX) and from Panama Canal transit information. Finally, after estimation of the global demand model for soybean traffic, we will discuss the implications for future soybean traffic through the waterway, evaluating alternative routes and sources for this trade.

Invasive scotch broom alters soil chemical properties in Douglas-fir forests of the Pacific Northwest, USA

2015 ◽  
Vol 398 (1-2) ◽  
pp. 281-289 ◽  
Author(s):  
Robert A. Slesak ◽  
Timothy B. Harrington ◽  
Anthony W. D’Amato
Keyword(s):  
Pacific Northwest ◽  
Chemical Properties ◽  
Douglas Fir ◽  
Soil Chemical Properties ◽  
Scotch Broom ◽  
The Pacific

scholarly journals Predicting Swiss Needle Cast Disease Distribution and Severity in Young Douglas-Fir Plantations in Coastal Oregon

2003 ◽  
Vol 93 (7) ◽  
pp. 790-798 ◽  
Author(s):  
Pablo H. Rosso ◽  
Everett M. Hansen
Keyword(s):  
Prediction Model ◽  
Disease Severity ◽  
Pacific Northwest ◽  
Regression Tree ◽  
Douglas Fir ◽  
Slope Aspect ◽  
Disease Prediction ◽  
Needle Cast ◽  
Swiss Needle Cast ◽  
The Relationship

Swiss needle cast (SNC), caused by the fungus Phaeocryptopus gaeumannii, is producing extensive defoliation and growth reduction in Douglas-fir forest plantations along the Pacific Northwest coast. An SNC disease prediction model for the coastal area of Oregon was built by establishing the relationship between the distribution of disease and the environment. A ground-based disease survey (220 plots) was used to study this relationship. Two types of regression approaches, multiple linear regression and regression tree, were used to study the relationship between disease severity and climate, topography, soil, and forest stand characteristics. Fog occurrence, precipitation, temperature, elevation, and slope aspect were the variables that contributed to explain most of the variability in disease severity, as indicated by both the multiple regression (r 2 = 0.57) and regression tree (RMD = 0.27) analyses. The resulting regression model was used to construct a disease prediction map. Findings agree with and formalize our previous understanding of the ecology of SNC: warmer and wetter conditions, provided that summer temperatures are relatively low, appear to increase disease severity. Both regression approaches have characteristics that can be useful in helping to improve our understanding of the ecology of SNC. The prediction model is able to produce a continuous prediction surface, suitable for hypothesis testing and assisting in disease management and research.

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