Compatible crown ratio and crown height models

1987 ◽  
Vol 17 (6) ◽  
pp. 572-574 ◽  
Author(s):  
Michael E. Dyer ◽  
Harold E. Burkhart
Keyword(s):  
Loblolly Pine ◽  
Tree Height ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Stand Age ◽  
Model Parameters ◽  
Crown Height ◽  
Pine Tree ◽  
Unrelated Regression ◽  
Tree Data

Several published crown ratio and crown height models were fitted to plantation loblolly pine tree data, but none were considered entirely adequate. A nonlinear model form that yields logical estimates is presented. Required inputs are stand age, tree diameter, and tree height. Both ordinary least squares and seemingly unrelated regression (SUR) were used to estimate model parameters. Cross equation constraints with the SUR procedure result in compatible estimates of crown ratio and crown height for a tree of given height.

Simultaneous estimation of biomass models for 13 tree species: effects of compatible additivity requirements

2017 ◽  
Vol 47 (6) ◽  
pp. 765-776 ◽  
Author(s):  
Thomas Nord-Larsen ◽  
Henrik Meilby ◽  
Jens Peter Skovsgaard
Keyword(s):  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Model Systems ◽  
Simultaneous Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Biomass Models ◽  
Unrelated Regression ◽  
Biomass Components ◽  
Species Specific

A desirable feature of biomass models distinguishing different tree components is compatible additivity of the component functions. Due to forcing of parameter estimates, such additivity is achieved at an expense of precision of the component functions. This study aimed to analyse the loss of precision incurred by forcing of parameters in tree biomass models due to (i) additivity constraints, (ii) combining global and species-specific parameters, and (iii) estimating component functions simultaneously as a system instead of as individual equations. Based on biomass data from 697 trees including 13 different species, we estimated a set of compatibly additive, nonlinear biomass models using simultaneous estimation and compared these with less restricted model systems. In line with other similar studies, the overall model system explained 88%–99% of the variation in individual biomass components. Compared with the unrestricted model, restricting parameters to obtain compatible additivity resulted in a change in RMSE of –0.6% to 5.4%. When restricting parameter estimates using both species-specific and global parameters, RMSE increased by 1.7%–13.1%. Estimating model parameters using simultaneous estimation (nonlinear iterated seemingly unrelated regression, NSUR) increased model bias compared with ordinary least squares estimation (OLS) for most biomass components. Contrary to expectations, NSUR estimation did not lead to a reduction in the standard error of estimates.

scholarly journals Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Shengwang Meng ◽  
Fan Yang ◽  
Sheng Hu ◽  
Haibin Wang ◽  
Huimin Wang
Keyword(s):  
Biomass Allocation ◽  
Tree Height ◽  
Northeastern China ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Model Parameters ◽  
Stem Biomass ◽  
Crown Width ◽  
Biomass Models ◽  
Mongolian Oak

Current models for oak species could not accurately estimate biomass in northeastern China, since they are usually restricted to Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) on local sites, and specifically, no biomass models are available for Liaodong oak (Quercuswutaishanica Mayr). The goal of this study was, therefore, to develop generic biomass models for both oak species on a large scale and evaluate the biomass allocation patterns within tree components. A total of 159 sample trees consisting of 120 Mongolian oak and 39 Liaodong oak were harvested and measured for wood (inside bark), bark, branch and foliage biomass. To account for the belowground biomass, 53 root systems were excavated following the aboveground harvest. The share of biomass allocated to different components was assessed by calculating the ratios. An aboveground additive system of biomass models and belowground equations were fitted based on predictors considering diameter (D), tree height (H), crown width (CW) and crown length (CL). Model parameters were estimated by jointly fitting the total and the components’ equations using the weighted nonlinear seemingly unrelated regression method. A leave-one-out cross-validation procedure was used to evaluate the predictive ability. The results revealed that stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branches increases with increasing D, H, CW and CL, while a reverse trend was found for bark and foliage. The root-to-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve the fitting results of the branch and foliage biomass models. We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

scholarly journals Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1778
Author(s):  
Wancai Zhu ◽  
Zhaogang Liu ◽  
Weiwei Jia ◽  
Dandan Li
Keyword(s):  
Tree Height ◽  
Information Criterion ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Pinus Koraiensis ◽  
Tree Level ◽  
Model Parameters ◽  
Crown Height ◽  
Crown Base Height ◽  
Pruning Intensity

Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

scholarly journals The Responsiveness of U.S. Rice Acreage to Price and Production Costs

1995 ◽  
Vol 27 (2) ◽  
pp. 386-399 ◽  
Author(s):  
Michael E. Salassi
Keyword(s):  
Production Cost ◽  
National Level ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Production Costs ◽  
Market Prices ◽  
Long Run ◽  
Unrelated Regression ◽  
Short Run ◽  
Average Annual Change

AbstractOrdinary least squares and seemingly unrelated regression procedures were used to analyze the impacts of changes in rice prices and production costs on U.S. rice planted acreage. National and regional response models were estimated over the 1970-92 period. Supply-inducing prices of rice were estimated as a function of effective rice support prices and seasonal average market prices. Expected production costs per acre were estimated using lagged actual total variable cash production expenses per acre adjusted by the previous 3-year average annual change in variable expenses. Estimated short-run price and production cost elasticities were found to be inelastic at the national level. However, the magnitude of the production cost elasticities were found to be greater than the price elasticities. Estimated long-run elasticities at the U.S. level were inelastic for changes in price but elastic for changes in production costs. Although acreage response varied across regions, similar relationships were found between price and production cost elasticities.

scholarly journals Gender Differences in Time in Child Care During Unemployment

2019 ◽  
Author(s):  
Margaret Gough
Keyword(s):  
Child Care ◽  
Time Use ◽  
The United States ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Unpaid Labor ◽  
Unrelated Regression ◽  
Parental Unemployment ◽  
Employed Parents ◽  
The Relationship

ObjectivesThere is great interest in the relationship between paid and unpaid labor time. Yet, in the United States most studies have focused on the housework component of unpaid labor. Limited research has examined how parental employment status relates to child care time. This study examines how unemployment is related to time in multiple types of child care and how this relationship varies by gender.MethodsI use data from the 2003-2013 American Time Use Surveys to study cohabiting and married parents ages 18-65 (N=44,198). I predict time spent in total child care, routine child care, and educational/recreational child care by parental unemployment status using ordinary least squares and seemingly unrelated regression models, and examine differences between weekday and weekend time use. ResultsConsistent with time-based theories, I find unemployed parents spend more time in child care than employed parents, but patterns vary by gender: unemployed mothers and fathers spend more time in child care on weekdays, but unemployed fathers spent less time in child care on weekends. ConclusionsResults suggest similarities and differences between the unemployment-child care time relationship and the relationship of unemployment with other types of unpaid labor such as housework.

scholarly journals Sectoral Analysis of the Determinants of Corporate Capital Structure in Malaysia

2019 ◽  
Vol 10 (2) ◽  
pp. 278-293
Author(s):  
Yee Peng Chow
Keyword(s):  
Capital Structure ◽  
Order Theory ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Pecking Order ◽  
Pecking Order Theory ◽  
Corporate Capital ◽  
Unrelated Regression ◽  
Corporate Capital Structure ◽  
Two Sectors

This study investigates the determinants of corporate capital structure of various sectors in the Bursa Malaysia Main Market with the aim to establish whether the determinants of capital structure can be explained by either the trade-off or the pecking order theory. This study also examines whether there are any differences between the regressions for any two sectors or not. This study applies both the ordinary least squares (OLS) and the seemingly unrelated regression (SUR) estimators to estimate the leverage models, and subsequently determines the efficiency of each estimator. The results indicate that profitability, asset tangibility, growth opportunities, and firm size are important determinants of corporate capital structure. However, the signs of the regression coefficients suggest that the trade-o and pecking order theories are complementary. Moreover, the importance of some of these determinants differs across sectors. In most cases of the regression analyses between two sectors, the SUR estimator is found to be more efficient in explaining the determinants of capital structure among the various sectors. Hence, this study concludes that the SUR method could serve as a useful alternative methodology for capital structure research.

scholarly journals The Determinants Of Capital Flight: Evidence From MENA Countries

2011 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Nedal A. Al-Fayoumi ◽  
Marwan S. AlZoubi ◽  
Bana M. Abuzayed
Keyword(s):  
Fixed Effects ◽  
External Debt ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Capital Flight ◽  
Macroeconomic Policies ◽  
Mena Countries ◽  
Real Gdp ◽  
Gdp Growth Rate ◽  
Unrelated Regression

This paper examines the determinants of capital flight in seven Middle East and North Africa (MENA) countries during the period of 1981-2008. The results are robust to four econometrics techniques: Ordinary least Squares, Fixed effects, Random Effects, and Seemingly Unrelated Regression Model. The empirical findings indicate that the capital flight in MENA countries is driven mainly by lag capital flight, external debt, foreign direct investment, real GDP growth rate and uncertainty. Based on these results, the paper recommends that governments in these countries should manage their external debt efficiently, and stabilize their monetary and macroeconomic policies in order to staunch capital flight.

An Evaluation of the Use of Ordinary Least Squares for Estimating Tourism Demand Models

1997 ◽  
Vol 35 (4) ◽  
pp. 69-73 ◽  
Author(s):  
Clive L. Morley
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Percentage Error ◽  
Regression Estimation ◽  
Tourism Demand ◽  
Constant Variance ◽  
Unrelated Regression ◽  
Demand Models ◽  
The Common

Estimating tourism demand models involves a set of related equations with errors that may not satisfy the common assumptions of being independent, with constant variance and normal distribution. In such circumstances, seemingly unrelated regression estimation may be considered a better estimation technique than ordinary least squares. Results from a simulation exercise, however, show that generally there is little difference between ordinary least squares and seemingly unrelated regression. The ordinary least squares technique performs well, and the results give little reason to use more complex estimation techniques. Another feature of tourism data is that strong growth in tourist numbers is often observed. This feature implies that models in which such series are the dependent variable are not consistently estimated by least squares methods. A percentage error loss function is proposed as a more appropriate criterion for estimating tourist data of this type.

A Comparison of Three Methods for Estimating Tourism Demand Models

1996 ◽  
Vol 2 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Clive L. Morley
Keyword(s):  
Least Squares ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Small Samples ◽  
Tourism Demand ◽  
Regression Methods ◽  
Unrelated Regression ◽  
Demand Models ◽  
The Common

Estimation of tourism demand models involves a set of related equations with errors which may not satisfy the common assumptions of regression modelling. Results from a simulation exercise show that, for the error types and small samples considered, the Generalized Method of Moments is less accurate on average than the Ordinary Least Squares and Seemingly Unrelated Regression methods, which had very similar accuracies. Overall, the Ordinary Least Squares technique performs well and the results give little reason to use the more complex estimation techniques.

scholarly journals Effectiveness of State-level Pecan Promotion Programs: The Case of the Texas Pecan Checkoff Program

HortScience ◽  
2009 ◽  
Vol 44 (7) ◽  
pp. 1914-1920 ◽  
Author(s):  
Eli D. Moore ◽  
Gary W. Williams ◽  
Marco A. Palma ◽  
Leonardo Lombardini
Keyword(s):  
Cost Analysis ◽  
Least Squares ◽  
State Level ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Regression Estimator ◽  
Benefit Cost Analysis ◽  
Unrelated Regression ◽  
Ordinary Least Squares Estimator ◽  
Benefit Cost

The Texas Pecan Board was established in 1998 to administer the Texas Pecan Checkoff Program and is financed through a half cent per pound assessment on grower pecan sales. The Board spends the assessment collections on a variety of advertising campaigns in an attempt to expand demand for Texas pecans and to increase the welfare of Texas pecan growers. This article presents an evaluation of the economic effectiveness of the Texas Pecan Checkoff Program in expanding sales of Texas pecans. First, the effects of Texas Pecan Board promotion on sales of all Texas pecans are determined using the ordinary least squares estimator followed by a test for differential effects of Texas Pecan Board promotion activities on sales of improved and native Texas pecan varieties using the seemingly unrelated regression estimator. The analysis indicates that the Texas Pecan Checkoff Program has effectively increased sales of improved varieties of Texas pecans but has had no statistically measurable impact on sales of native varieties of Texas pecans. A benefit–cost analysis determines that $35.0 in additional sales revenues are generated for every dollar invested in promotion, indicating that the Texas pecan promotion program has been financially successful. The per unit return is large but on a very few dollars available for investment in promotion implying the need for more investment for more meaningful returns.

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