## 1 Introduction  
Carbon fixation by plant biomass units promotes reduction of concentration of greenhouse gases in the atmosphere, thereby lessening global warming [1; 2; 3; 4; 5; 6]. Therefore, the assessment of the flux and storage of carbon in plant biomass is of great interest. Concomitantly important is the adaptation of methods aimed at non-destructive estimation. For instance, aboveground tree biomass in forest ecosystems has been estimated through remote sensing protocols [7; 8; 9; 10]. Nevertheless, a number of factors such as sample size, weather, complexity of biophysical settings, study area scale, software, or spatial resolution can induce uncertainty of remote-sensed estimation [11; 12; 13; 14; 15]. Allometric methods allow implementation of parallel cost-effective non-destructive estimation of plant biomass units [16; 17; 18; 19; 20; 21; 22; 23; 24]. However, this approach is not also problem-free. Factors like analysis method sample size and data quality can bear significant influences on precision [25; 26; 27; 28; 29; 30; 31; 32; 33]. Understanding the way procedural factors drive precision of allometric projection of plant biomass units is crucial for assuring reliability. This paper centers on the influences of the analysis method and particularly aims at proposing reliable methods for handling complexity. For clarification it is pertinent reviewing the notions behind interpretation and identification of the allometric paradigm. As was originally envisioned, the term allometry, also mentioned as biological scaling, refers to the relation between the size of a given organismal trait and overall body size. The notion developed from observations by [PERSON] in 1892 and [PERSON] in 1917. Allometry as a study subject was outlined by [PERSON] in 1932, in his theory of constant relative growth by two body parts [34], formulated though the scaling equation:  
\[y=\beta x^{\alpha} \tag{1}\]  
where \(y\) and \(x\) are quantifiable traits, the parameter \(\alpha\) is nominated as the allometric exponent and \(\beta\) is recognized as the normalization constant. This model also known as the equation of simple allometry has been widely used in research problems in many fields including, biology [25; 35; 36], biomedical sciences [37; 38; 39; 40; 41], economics [42; 43; 44; 45; 46], earth and planetary sciences [47; 48; 49; 50; 51], resource management and conservation [52; 53; 54; 55; 56]. The interest for this model lies essentially in its practical utility to produce surrogates of a response \(y\), that is difficult to measure in direct way, by using estimates of the parameters \(\beta\) and \(\alpha\) and easily gotten measurements of a covariate \(x\). Parallel to Equation (1) is the traditional analysis method of allometry (TAMA). This is a widespread protocol that relies in loftransformation in order to transfer Equation (1) into a linear model in geometrical space. Then, the fitted line is back-transformed to yield the original two parameter power function in arithmetical scale. A loftransformation embraces a notion of multiplicative growth. Moreover, in [PERSON]'s rationale the intercept \(ln\beta\) of TAMA's line was of no explicit biological relevance, but the slope \(\alpha\) was significant enough as to mean allometry itself. This interpretation maintains nowadays as the only valid theoretical perspective for many practitioners of allometry [57; 58; 59]. However, in spite of TAMA's prevalence, there are views asserting this scheme produces biased results [60; 61; 62; 63; 64]. Also allometrical relationships express as power functions and fit in to a non-linear form in the original scale of data. Thus, keeping the analysis in arithmetical scales is in some way more adequate. Concomitantly for this perspective, direct non-linear regression in arithmetical scales (DNLR) becomes a default standard [65; 66; 67; 68; 69; 70; 71; 72]. From this slant, the failure of a TAMA fit many obeys to unsuitable complexity endured by [PERSON]'s formula of simple allometry.