Allometric models for predicting above- and belowground biomass of Leucaena-KX2 in a shaded coffee agroecosystem in Hawaii

2011 ◽  
Vol 83 (3) ◽  
pp. 331-345 ◽  
Author(s):  
Adel H. Youkhana ◽  
Travis W. Idol
Keyword(s):  
Belowground Biomass ◽  
Allometric Models

Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania

2013 ◽  
Vol 310 ◽  
pp. 87-101 ◽  
Author(s):  
Wilson Ancelm Mugasha ◽  
Tron Eid ◽  
Ole Martin Bollandsås ◽  
Rogers Ernest Malimbwi ◽  
Shabani Athumani Omari Chamshama ◽  
...  
Keyword(s):  
Belowground Biomass ◽  
Miombo Woodlands ◽  
Allometric Models

Development of allometric models for above and belowground biomass in swidden cultivation fallows of Northern Laos

2015 ◽  
Vol 357 ◽  
pp. 104-116 ◽  
Author(s):  
Iain M. McNicol ◽  
Nicholas J. Berry ◽  
Thilde Bech Bruun ◽  
Kristell Hergoualc’h ◽  
Ole Mertz ◽  
...  
Keyword(s):  
Belowground Biomass ◽  
Swidden Cultivation ◽  
Allometric Models

scholarly journals Allometric derivation and estimation of Guadua weberbaueri and G. sarcocarpa biomass in the bamboo-dominated forests of SW Amazonia

2017 ◽  
Author(s):  
Noah Yavit
Keyword(s):  
Full Range ◽  
Belowground Biomass ◽  
Growth Patterns ◽  
Total Carbon ◽  
Stem Length ◽  
Bamboo Species ◽  
Branch Number ◽  
Allometric Models ◽  
Small Branch ◽  
Guadua Weberbaueri

AbstractBamboo-dominated forests in Southwestern Amazonia encompass an estimated 180,000 km2 of nearly contiguous primary, tropical lowland forest. This area, largely composed of two bamboo species, Guadua weberbaueri Pilger and G. sarcocarpa Londoño & Peterson, comprises a significant portion of the Amazon Basin and has a potentially important effect on regional carbon storage. Numerous local REDD(+) projects would benefit from the development of allometric models for these species, although there has been just one effort to do so. The aim of this research was to create a set of improved allometric equations relating the above and belowground biomass to the full range of natural size and growth patterns observed. Four variables (DBH, stem length, small branch number and branch number ≥ 2cm diameter) were highly significant predictors of stem biomass (N≤ 278, p< 0.0001 for all predictors, complete model R2=0.93). A secondary field model (containing DBH and branch number > 2cm diameter), proved highly significant as well (N= 278, p< 0.0001 for both predictors, R2=0.84). The belowground biomass was estimated to be 19.2±6.2% of the total dry biomass of the bamboo species examined. To demonstrate the utility of these models in the field and derive stand-level estimates of bamboo biomass, ten 0.36-ha plots were analyzed (N= 3,966 culms), yielding above + belowground biomass values ranging from 4.3–14.5 Mg·ha-1. The results of this research provide novel allometric models and estimates of the contribution of G. weberbaueri and G. sarcocarpa to the total carbon budget of this vast and largely unexplored Amazonian habitat.

scholarly journals Site-Specific Allometric Models for Prediction of Above-and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 862 ◽  
Author(s):  
Zhao ◽  
Li ◽  
Zhou ◽  
Qiu ◽  
Wu
Keyword(s):  
Southern China ◽  
Belowground Biomass ◽  
Subtropical Forest ◽  
Forest Carbon ◽  
Total Biomass ◽  
Carbon Sinks ◽  
Site Specific ◽  
Subtropical Forests ◽  
Allometric Models ◽  
Biomass Models

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.

BERM: a Belowground Ecosystem Resiliency Model for estimating Spartina alterniflora belowground biomass

2021 ◽  
Author(s):  
Jessica L. O’Connell ◽  
Deepak R. Mishra ◽  
Merryl Alber ◽  
Kristin B. Byrd
Keyword(s):  
Spartina Alterniflora ◽  
Belowground Biomass

scholarly journals Aboveground Biomass Allocation of Boreal Shrubs and Short-Stature Trees in Northwestern Canada

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 234
Author(s):  
Linda Flade ◽  
Christopher Hopkinson ◽  
Laura Chasmer
Keyword(s):  
Short Stature ◽  
Biomass Allocation ◽  
Aboveground Biomass ◽  
Leaf Biomass ◽  
Sectional Area ◽  
Plant Component ◽  
Cross Sectional ◽  
Allometric Models ◽  
Stem Biomass ◽  
Model Fits

In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein ‘AGB’) as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (<4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function.

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