scholarly journals Individual Tree Volume Equations for Red Pine in Michigan

1997 ◽  
Vol 14 (2) ◽  
pp. 53-58 ◽  
Author(s):  
Gary W. Fowler
Keyword(s):  
Red Pine ◽  
Data Sets ◽  
Regression Equations ◽  
Independent Data ◽  
Data Set ◽  
Individual Tree ◽  
Standing Trees ◽  
Pine Stands ◽  
Volume Estimates ◽  
Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

New Form-class Equations Improve Volume Estimates

1973 ◽  
Vol 3 (3) ◽  
pp. 338-341
Author(s):  
F. Evert
Keyword(s):  
Ground Level ◽  
Red Pine ◽  
Stem Diameter ◽  
Individual Tree ◽  
Stand Volume ◽  
Above Ground Level ◽  
Form Class ◽  
New Form ◽  
Volume Estimates ◽  
Volume Equations

Three form-class volume equations involving the upper stem diameter at 19.5 ft (5.94 m) above ground level and three standard volume equations based on d.b.h. and height were tested for accuracy in estimating both tree and stand volume in different stands of red pine (Pinusresinosa Ait.). All three form-class equations met the required 10% accuracy in estimating individual tree volumes; the three standard volume equations failed to meet this accuracy. All three form-class equations also met the required 5% accuracy in estimating stand volume, but none of the standard equations did.

scholarly journals STRATEGIES FOR STEM MEASUREMENT SAMPLING: A STATISTICAL APPROACH OF MODELLING INDIVIDUAL TREE VOLUME

CERNE ◽  
2016 ◽  
Vol 22 (3) ◽  
pp. 249-260 ◽  
Author(s):  
Hassan Camil David ◽  
Rodrigo Otávio Veiga Miranda ◽  
John Welker ◽  
Luan Demarco Fiorentin ◽  
Ângelo Augusto Ebling ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Statistical Tests ◽  
Fixed Number ◽  
Stem Volume ◽  
Diameter Class ◽  
Clear Trend ◽  
Data Set ◽  
Individual Tree ◽  
Volume Estimates ◽  
Volume Equations

ABSTRACT The aim of this paper was to evaluate different criteria for stem measurement sampling and to identify the criterion with best performance for developing individual tree volume equations. Data were collected in eucalyptus stands 58 to 65 months old. Schumacher-Hall model was applied using five sampling criteria with nine variations (45 in total): 1) number of trees per diameter class, being (a) fixed number, (b) proportional to the diameter class of the sample, or (c) proportional to the standard deviation of the sample; and 2) the width of the diameter class, which ranged from 1.0 up to 5.0 cm. We used the equations generated from each of the five sampling criteria to estimate stem volume of trees reserved for validation. This allowed us to obtain standard errors of estimates from this data-set. In addition, residuals of volume estimates were examined by means of statistical tests of bias, autocorrelation and heteroscedasticity. Better performances of volume equations occurred when smaller diameter class widths were used, i.e., when the sample size increased. There was no clear trend in increasing/decreasing residual autocorrelation and/or heteroscedasticity. Both methods of sampling proportional to the frequency of diameter class had the best performances, inclusive using only 36 trees. The ones where choice of trees was proportional to the standard deviation had the worst. In conclusion, the selection proportional to the frequency of the diameter class, under the condition that at least two trees per class are sampled, provides models statistically better than all the other criteria.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

Estimation de la production de jeunes plantations de pin rouge et de pin gris du sud du Québec

1976 ◽  
Vol 6 (4) ◽  
pp. 478-486 ◽  
Author(s):  
H. A. Bolghari
Keyword(s):  
Multiple Regression ◽  
Jack Pine ◽  
Red Pine ◽  
Pine Plantations ◽  
Regression Equations ◽  
Pine Plantation ◽  
South Shore ◽  
Pine Stands ◽  
Natural Stands ◽  
St Lawrence River

Multiple regression equations have been developed to predict yield from young red pine and jack pine plantations. Data from 446 sample plots representing young red pine and jack pine stands located on the south shore of the St. Lawrence River between Quebec and Montreal were analysed. The red pine plantation yielded more than the jack pine. However, in plantation both species yield more than in natural stands. Taking into account the age and spacing of the sampled plantations, the equation obtained can provide information on yield of red pine and jack pine stands the maximum spacing of which is 3 × 3 m, up to the age of 45 and 35 years respectively. The equations will allow the construction of preliminary yield tables for both species.

scholarly journals Taper, Volume, and Weight Equations for Red Pine in West Virginia

2008 ◽  
Vol 25 (3) ◽  
pp. 151-153 ◽  
Author(s):  
Lichun Jiang ◽  
John R. Brooks
Keyword(s):  
West Virginia ◽  
Ease Of Use ◽  
Red Pine ◽  
Seemingly Unrelated Regression ◽  
Stem Volume ◽  
West Virginia University ◽  
Individual Tree ◽  
Unrelated Regression ◽  
Taper Equation ◽  
Volume Equations

Abstract Compatible taper, volume, and weight equations were developed for planted red pine in West Virginia. The data were based on stem analysis of 26 trees from West Virginia University Research Forest, located in northern West Virginia. A commonly used segmented polynomial taper equation was chosen because of its balance between prediction accuracy and ease of use. Seemingly unrelated regression was used to simultaneously fit the system of equations for inside and outside bark data. When compared with existing total stem volume equations developed by Fowler (Fowler, G.W., 1997, Individual tree volume equations for red pine in Michigan, North. J. Appl. For. 14:53–58) and by Gilmore et al. (Gilmore, D.W., et al., 2005, Thinning red pine plantations and the Langsaeter hypothesis: A northern Minnesota case study. North, J. Appl. For. 22:19–25), a positive bias was evident that increased directly with stem diameter for trees from this region.

A Ccomparison of sedigraph and pipette methods for soil particle-size analysis

Soil Research ◽  
1993 ◽  
Vol 31 (4) ◽  
pp. 407 ◽  
Author(s):  
GD Buchan ◽  
KS Grewal ◽  
JJ Claydon ◽  
RJ Mcpherson
Keyword(s):  
Particle Size ◽  
New Zealand ◽  
Particle Size Analysis ◽  
Size Analysis ◽  
Data Sets ◽  
Regression Equations ◽  
Total Iron Content ◽  
Data Set ◽  
X Ray ◽  
The Difference

The X-ray attenuation (Sedigraph) method for particle-size analysis is known to consistently estimate a finer size distribution than the pipette method. The objectives of this study were to compare the two methods, and to explore the reasons for their divergence. The methods are compared using two data sets from measurements made independently in two New Zealand laboratories, on two different sets of New Zealand soils, covering a range of textures and parent materials. The Sedigraph method gave systematically greater mass percentages at the four measurement diameters (20, 10, 5 and 2 �m). For one data set, the difference between clay (<2 �m) percentages from the two methods is shown to be positively correlated (R2 = 0.625) with total iron content of the sample, for all but one of the soils. This supports a novel hypothesis that the typically greater concentration of Fe (a strong X-ray absorber) in smaller size fractions is the major factor causing the difference. Regression equations are presented for converting the Sedigraph data to their pipette equivalents.

scholarly journals Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Miles Grafton ◽  
Therese Kaul ◽  
Alan Palmer ◽  
Peter Bishop ◽  
Michael White
Keyword(s):  
Soil Nutrient ◽  
Large Data ◽  
Technical Note ◽  
Soil Samples ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
Hill Country ◽  
Olsen P

This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field.

A two-stage matrix model for predicting growth of forest stands in the Northeast

1986 ◽  
Vol 16 (3) ◽  
pp. 521-528 ◽  
Author(s):  
Dale S. Solomon ◽  
Richard A. Hosmer ◽  
Homer T. Hayslett Jr.
Keyword(s):  
Diameter Distribution ◽  
Data Sets ◽  
Independent Data ◽  
Forest Types ◽  
Diameter Class ◽  
Northern Hardwood ◽  
Harvest Intervals ◽  
Thinning Operations ◽  
Volume Estimates ◽  
Diameter Classes

Matrices are used to model ingrowth, survivor growth, and mortality for stands in different forest types in the Northeast. Equations are developed for several species from softwood and northern hardwood stand data estimating the probability of trees remaining in a diameter class, increasing one or two diameter classes, or dying. By knowing species composition and diameter distribution, FIBER predicts stand yields for managed and unmanaged stands with densities ranging from 9.2 to 41.3 m2/ha using 5-year iterations. Actual and predicted volume estimates from independent data sets are compared for different species compositions, densities, thinning operations, and harvest intervals in the softwood, northern hardwood, and mixed-wood forest types.

Estimation of individual tree volumes with age and diameter

1983 ◽  
Vol 13 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Riyaz A. Sadiq ◽  
Victor G. Smith
Keyword(s):  
Measurement Errors ◽  
Analysis Data ◽  
Red Pine ◽  
Stem Analysis ◽  
Hilly Terrain ◽  
Individual Tree ◽  
Southern Ontario ◽  
Standard Methods ◽  
Standing Trees ◽  
Individual Trees

Even though invention of high-precision equipment has reduced measurement errors associated with the estimation of heights of standing trees, height estimation is still an expensive and time-consuming operation. At times it is difficult to determine especially in dense forests or in forests located in hilly terrain. The present study advocates a volume–age–diameter function to estimate volumes of individual trees. The technique presented here circumvents measurement of tree heights through the use of age which, however, restricts the application of the function to plantations or forests whose age is predetermined. Analyses with stem-analysis data from red pine (Pinusresinosa Ait.) plantations of southern Ontario indicate that the function estimates tree volumes more accurately than the standard methods commonly used.

Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?

2021 ◽  
pp. 001316442110235
Author(s):  
Wilhelmina van Dijk ◽  
Christopher Schatschneider ◽  
Stephanie Al Otaiba ◽  
Sara A. Hart
Keyword(s):  
Measurement Invariance ◽  
Model Fit ◽  
Data Sets ◽  
Independent Data ◽  
Power Combining ◽  
Data Set ◽  
Pooled Data ◽  
The Difference ◽  
User Friendly ◽  
Adequate Power

Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.

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