Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine
Sustainable forest management requires accurate estimates of wood volume. At present, red pine (<i>Pinus resinosa</i> Sol. ex Aiton) is the most widely planted conifer tree species in southern Ontario, Canada. Therefore, inside and outside bark volume equations were developed for red pin...
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Forests |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4907/11/10/1050 |
id |
doaj-fd6333ea2118421bb52c4518b7829d2a |
---|---|
record_format |
Article |
spelling |
doaj-fd6333ea2118421bb52c4518b7829d2a2020-11-25T02:55:05ZengMDPI AGForests1999-49072020-09-01111050105010.3390/f11101050Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red PineMahadev Sharma0Ontario Forest Research Institute, Ministry of Natural Resources and Forestry, 1235 Queen St. East, Sault Ste. Marie, ON P6A 2E5, CanadaSustainable forest management requires accurate estimates of wood volume. At present, red pine (<i>Pinus resinosa</i> Sol. ex Aiton) is the most widely planted conifer tree species in southern Ontario, Canada. Therefore, inside and outside bark volume equations were developed for red pine trees grown in plantations. One hundred and fifty red pine trees were sampled from 30 even-aged plantations from across Ontario, Canada. Height-diameter pairs along the boles of sampled trees used to calculate stem volumes were obtained from stem analysis. Equations fitted to the data were a combined variable, modified combined variable, and modified form of dimensionally compatible volume equations. These equations were compared for their goodness-of-fit statistics, logical consistency, and predictive accuracy. The goodness-of-fit characteristics for all three equations were comparable for both inside and outside bark volumes. However, the estimated values for the intercept for the modified form of the dimensionally compatible and modified combined variable volume equations were negative and nonsignificant. The combined variable volume equation resulted in logically consistent parameter estimates in the presence of random effects parameters. Therefore, this equation was selected as the inside and outside bark volume equation for red pine trees grown in plantations. A nonlinear mixed-effects modeling approach was applied in fitting the final volume equation that included a weight (power function) to address heteroscedasticity. The equations developed here can be used to calculate inside and outside bark volumes of red pine plantations in boreal forests in Eastern Canada. These equations would require both diameter at breast height (DBH) and total height values in meters.https://www.mdpi.com/1999-4907/11/10/1050tree formvolume and biomasstaper equationproduct recoverymixed-effects models |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahadev Sharma |
spellingShingle |
Mahadev Sharma Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine Forests tree form volume and biomass taper equation product recovery mixed-effects models |
author_facet |
Mahadev Sharma |
author_sort |
Mahadev Sharma |
title |
Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine |
title_short |
Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine |
title_full |
Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine |
title_fullStr |
Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine |
title_full_unstemmed |
Increasing Volumetric Prediction Accuracy—An Essential Prerequisite for End-Product Forecasting in Red Pine |
title_sort |
increasing volumetric prediction accuracy—an essential prerequisite for end-product forecasting in red pine |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2020-09-01 |
description |
Sustainable forest management requires accurate estimates of wood volume. At present, red pine (<i>Pinus resinosa</i> Sol. ex Aiton) is the most widely planted conifer tree species in southern Ontario, Canada. Therefore, inside and outside bark volume equations were developed for red pine trees grown in plantations. One hundred and fifty red pine trees were sampled from 30 even-aged plantations from across Ontario, Canada. Height-diameter pairs along the boles of sampled trees used to calculate stem volumes were obtained from stem analysis. Equations fitted to the data were a combined variable, modified combined variable, and modified form of dimensionally compatible volume equations. These equations were compared for their goodness-of-fit statistics, logical consistency, and predictive accuracy. The goodness-of-fit characteristics for all three equations were comparable for both inside and outside bark volumes. However, the estimated values for the intercept for the modified form of the dimensionally compatible and modified combined variable volume equations were negative and nonsignificant. The combined variable volume equation resulted in logically consistent parameter estimates in the presence of random effects parameters. Therefore, this equation was selected as the inside and outside bark volume equation for red pine trees grown in plantations. A nonlinear mixed-effects modeling approach was applied in fitting the final volume equation that included a weight (power function) to address heteroscedasticity. The equations developed here can be used to calculate inside and outside bark volumes of red pine plantations in boreal forests in Eastern Canada. These equations would require both diameter at breast height (DBH) and total height values in meters. |
topic |
tree form volume and biomass taper equation product recovery mixed-effects models |
url |
https://www.mdpi.com/1999-4907/11/10/1050 |
work_keys_str_mv |
AT mahadevsharma increasingvolumetricpredictionaccuracyanessentialprerequisiteforendproductforecastinginredpine |
_version_ |
1724718331239333888 |