View other plants in this family QR code link View other plants in this genus Introduction Lebombo-ironwood is an evergreen tree which occurs in hot savannas; it is reputed for its hard and long-lasting wood. Many strong structures around the region where it occurs are built from it and it has even been used to build bridges. The species makes an attractive ornamental with its attractive, deep green, rounded leaves, which are white on the underside. This tree grows well in rocky areas.

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The root system was divided into 3 subcomponents: fine lateral roots, coarse lateral roots, and taproot. First, the root system was partially excavated to the first node, using hoes, shovels, and picks, to expose the primary lateral roots Figures 1 a —1 c.

The primary lateral roots were numbered and separated from the taproot with a chainsaw Figures 1 b and 1 c and removed from the soil, one by one. This procedure was repeated in the subsequent nodes until all primary roots were removed from the taproot and the soil. Finally, the taproot was excavated and removed Figures 1 d —1 f.

Fresh weight was obtained for the taproot, each coarse lateral root and for all fine lateral roots. A sample was taken from each subcomponent, fresh weighed, marked, packed in a bag, and taken to the laboratory for oven drying.

For the taproot, the samples were two discs, one taken immediately below the ground level and another from the middle of the taproot. For the coarse lateral roots, two discs were also taken, one from the insertion point on the taproot and another from the middle of it. Stem Wood and Stem Bark. Felled trees were scaled up to a 2. The stem was defined as the length of the trunk from the stump to the height that corresponded to 2.

The remainder from the height corresponding to 2. The stem was divided into sections, the first with 1. Discs were removed on the bottom and top of the first section and on the top of the remaining sections; that is, discs were removed at heights of 0. Diameters over and under bark were taken from the discs in the North-South direction previously marked on the standing tree with the help of a ruler.

Bark volume was obtained from the difference between volume over bark and volume under bark. The discs were dipped in drums filled with water for its saturation 3 to 4 months and subsequent determination of the saturated volume and basic density.

This procedure was done twice: before and after debarking; hence, we obtained saturated volume under and over the bark.

Basic density was obtained by dividing the oven dry weight of the discs with and without bark by the relevant saturated wood volume [27, 30]. Therefore, two distinct basic densities were calculated: 1 basic density of the discs with bark and 2 basic density of the discs without bark.

We estimated the basic density at point of geometric centroid of each section using the regression function of density over height [31]. This density value was taken as representative of each section [31]. The crown was divided into two subcomponents: branches and foliage.

Large branches were sampled similarly to coarse roots, and fine branches and foliage were sampled similarly to fine roots. All the leaves from each tree were collected and fresh weighed together and a sample was taken for oven drying. The subcomponents branches and foliage were not treated as separated components because in the preliminary analysis the weight of the foliage did not show significant variation with DBH, H, CH, and LCL, exhibiting, therefore, poor fits.

International Journal of Forestry Research 2. Tree Component Dry Weights. Dry weights of each stem section with and without bark were obtained by multiplying respective densities by relevant stem section volumes. The dry weight of the stem bark was obtained from the difference between the dry weights of stem and stem wood.

Finally, the total tree biomass was obtained by adding the component dry weights. Data Analysis. Several linear and nonlinear regression model forms were tested for each tree component and for the total tree using weighted least squares WLS. The weight functions were obtained by iteratively finding the optimal weight that homogenises the residuals and improves other fit statistics.

Independent tree component models were fitted with the statistical software package R [32] and the functions lm and nls for linear models and nonlinear models the latter of which using the Gauss-Newton algorithm.

The best linear and nonlinear biomass equations selected are given in 1 and 2 , respectively. Although, the selected weight function might not be the best one among all possible weights, it is the best approximation found. For this method, the most frequent best linear model form in 1 among tree components was used for all other components and for total tree biomass. Table 3: Fit statistics of SUR using the system of the best linear models. The SUR method consisted of first fitting and selecting the best linear models for each tree component.

The total tree model was a function sum of the independent variables used in each tree component model. Then, all models, including the total, were fitted again simultaneously using joint-generalized least squares also known as SUR under the restriction of the coefficients of regression, which ensured additivity.

This requirement is not verified, as three of the four components have identical regressors. Indeed, according to Srivastava and Giles [34], applying SUR to system of the best equations given above is of no benefit when the component equations have identical explanatory variables. Moreover, as stated by Greene [35] and Bhattacharya [36], a system of linear SUR equations with identical regressors yields ineffective estimates of coefficient vectors when compared to equation-by-equation ordinary least squares OLS.

To eliminate the ineffectiveness caused by identical regressors, SUR was applied using second best regression equations for belowground and stem wood biomasses such that the different tree component equations could have different regressors. The resulting system of equations of biomass additivity is given in 4. However, the results of SUR using the best independent model forms are given in Tables 2 and 3, for demonstration proposes of the ineffectiveness caused by identical regressors.



Yozshugul The rates of shrinkage are moderate, from green to oven dry 5. Androstachys johnsonii is locally common and may even form dense thickets. Citation in scholarly articles. Young plants are not frost tolerant. It is valued for furniture for both indoor nohnsonii outdoor uses, and for sculptures androstacchys turnery.


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