An Application of Generalized Additive Models of Location, Scale, and Shape (GAMLSS) to estimate the Eucalyptus Height

Abstract

The Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are a recent class of models that further flexibilitythe distribution of the response variable. The regression analysis has been used to model biological phenomena, and its variousmodalities have met the need for its use with precision. However, there are situations in which the adjustment of models with moreflexible assumptions in the specification of the distribution of the response variable becomes indispensable, thus justifying the useof GAMLSS. The study of plant growth curves has full application in agricultural research; thus, it is crucial to know the habits ofgrowth and development of forest species is crucial for reforestation programs and in the most diverse researches. The study aimedto model the growth of Eucalyptus through the adjusting of Generalized Additive Models for Location, Scale, and Shape, in orderto promote improvements on crop productivity. Considering all parameters of the independent variable (time) under GAMLSSclass modeling, the distribution model ST3 presented better results.

A GAMLSS approach to predicting growth of Nopalea cochenillifera Giant Sweet clone submitted to water and saline stress

“…The BCPE model was considered to be well adjusted because quantile residues were within the limits of ±3 (Figure 3a) and theoretical quantiles closely followed the BCPE distribution (Figure 3b), with no inadequacy in the response variable distribution; the same behavior was reported by Fumes et al (2017) and Oliveira et al (2020) in their evaluation of Eucalyptus height.…”

“…Recently, GAMLSS techniques have been applied to growth curve adjustment, as reported by Oliveira et al (2020), who used a Skew-t type 3 (ST3) distribution, and Fumes, Demétrio, Villegas, Corrente, and Bazzo (2017), who used a Box-Cox normal (BCN) distribution to estimate height of Eucalyptus species. However, our literature search found no reports of using GAMLSS for modeling in pasture and forage studies; instead, response variable modeling has been evaluated using only simple or multiple linear regression models (Reis et al, 2016;Freitas et al, 2018), or generalized linear models (Lucena et al, 2018b).…”