Height-diameter allometry for tropical forest in northern Amazonia

Height measurements are essential to manage and monitor forest biomass and carbon stocks. However, accurate estimation of this variable in tropical ecosystems is still difficult due to species heterogeneity and environmental variability. In this article, we compare and discuss six nonlinear allometric models parameterized at different scales (local, regional and pantropical). We also evaluate the height measurements obtained in the field by the hypsometer when compared with the true tree height. We used a dataset composed of 180 harvested trees in two distinct areas located in the Amapá State. The functional form of the Weibull model was the best local model, showing similar performance to the pantropical model. The inaccuracy detected in the hypsometer estimates reinforces the importance of incorporating new technologies in measuring individual tree heights. Establishing accurate allometric models requires knowledge of ecophysiological and environmental processes that govern vegetation dynamics and tree height growth. It is essential to investigate the influence of different species and ecological gradients on the diameter/height ratio.

Allometric Models for Estimating Aboveground Biomass in Short Rotation Crops of Acacia Species in Two Different Sites in Chile

We evaluated the ability of different allometric models to estimate the biomass production of short-rotation woody crops of Acacia dealbata, A. mearnsii and A. melanoxylon. Models considered the adjustment and validation of biomass functions and biological restrictions, such as the use of additive components of the biomass (stem, branches, and leaves). Adjustments of linear and nonlinear models of the three acacia species—established in two locations and of three densities in southern Chile—were utilized. Systems of equations were adjusted to guarantee the addition of the biomass components and the trees’ total biomass. The selection of models was performed based on their goodness of fit and predictive quality. Methods that accounted for the correlation between biomass components granted an additively consistent equations system with efficient estimates and reliable prediction intervals.

An Allometric Model Based Approach For Estimating Biomass In Seven Indian Bamboo Species In Western Himalayan Foothills, India

The rapid growth rate, high biomass production, and annual harvesting, makes bamboo as suitable species for commercial production. Allometric equations for many broadleaf and conifer tree species are available. However, knowledge on biomass production and allometric equations of bamboos are limited. This study aims at developing species specific allometric models for predicting biomass and synthetic height values as a proxy variable for seven bamboo species in Himalayan foothills. Two power form based allometric models were used to predict above ground and culm biomass using Diameter at breast height (D) alone and D in combination with culm height (H) as independent variable. This study also extended to establishing H-D allometric model that can be used to generate synthetic H values as proxy to missing H. In the seven bamboo species studied, among three major biomass component (culm, branch and foliage), culm is the most important component with highest share (69.56 to 78.71%).Distribution of percentage (%) share of culm, branch and foliage to above ground fresh weight varies significantly between different bamboo species. D. hamiltonii has highest productivity for above ground biomass components. Ratio of dry to fresh weight of seven bamboo species was estimated for culm, branch, foliage and above ground biomass to convert fresh weight to dry weight.

Estimating Yield Components, Limiting Factors, and Yield Gaps of Enset in Ethiopia Using Easily Measurable Above-Ground Plant Traits

The quantification of yield for different enset products has mainly been based on farmers’ estimates, which are often inaccurate. Several allometric models have been developed to overcome this challenge. Building on past work, the current study developed allometric models for enset fiber, kocho, and bula yield estimation. Enset yield limiting factors and associated yield gaps were also determined. In this study, above-ground growth and yield (kocho, bula, and fiber) traits of five-year-old plants of two widely grown enset landraces, ‘Unjame’ and ‘Siskela’, were assessed in farmers’ fields at three contrasting altitude sites. Except for bula, a minor yield component, correlation, and PCA analysis showed strong association between the above-ground and yield traits. Allometric equations based on the above-ground traits significantly (R2 = 25 to 68%) explained the variation in the yield traits. This study, for the first time, generated allometric models that can reliably estimate enset fiber yield. Leaf length, petiole length, and plant height are especially good for estimating fiber and kocho yields. The performance of models for bula were poor possibly due to the very low bula yields per plant. Soil chemical characteristics differently influenced enset yield attributes. For example, improving K supply can potentially enhance fiber yield. Higher yield gaps were observed for bula, with P accounting for the highest yield gaps across yield traits. Through careful targeting, the different yield attributes can thus be enhanced. This and previous studies clearly show that non-destructive enset plant assessments can provide solid information for quick and easy yield assessments for various traits during e.g., agronomic, germplasm evaluation, soil fertility enhancement, and intercropping trials.

Estimating Aboveground Biomass Using Allometric Models And Adaptive Learning Rate Optimization Algorithms

Forest aboveground biomass (AGB) is imperative in the study of climate change and the carbon cycle in the global terrestrial ecosystem. Developing a credible approach to estimate forest biomass and carbon stocks is essential. Four allometric models were used with two optimization algorithms; Modified Root Mean Square Propagation (Modified RMSProp) and Modified Adaptive Moment Estimation (Modified Adam) were also used to train each model. Convergence was achieved after 1000 iterations of Modified RMSProp and 200 iterations of Modified Adam for all the models. A learning rate of 0.01 and exponential decay rates of 0.9 and 0.999 for the first and second momentum. A loss function of 0.5 Mean Square Error (0.5 MSE) was used and Root Mean Square Error (RMSE) was used to judge the accuracy of the models. The study showed that the optimization algorithms were both able to accurately optimize three of the four allometric models. While Modified Adam was the more efficient optimizer, it had the highest RMSE value 2.3910 and Modified RMSProp had the least RMSE value 0.37381. However, there was no statistically significant difference between the accuracy of the models optimized by both algorithms.

Non-destructive allometric models for leaf area prediction of Jatropha curcas L.

Jatropha curcas received a lot of attention as a biodiesel feedstock in several countries around the world. The leaf area (LA) estimation is important for biological research due to its close relationship to soil fertility, plant physiological parameters, and photosynthetic efficiency. Allometric models for reliable and accurate models for estimating the leaf area of Jatropha curcas plants based on non-destructive measurements of leaf length (L), width (W), and/ or leaf margins length (P) were developed. Jatropha plant leaves were randomly selected for model construction to estimate the leaf area using L, W, and P leaf values. Simple and accurate equations were obtained, based on leaf width (AL = W1.795), R2 = 0.987, rib length (AL = L32.081), R2 = 0.989, width and rib length (AL = LW0.320), R2 = 0.997, rib length and leaf periphery segment (AL = LP1P120.274), R2 = 0.995, and half rib and width length (AL = LW/0.50.467), R2 = 0.994. The equations were accurate for estimating leaf area of small, medium, and large size of Jatropha leaves.