Nonlinear quantile regression to describe the dry matter accumulation of garlic plants

Abstract

The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

Quantile regression for genomic selection of growth curves

“…Nonlinear QR to describe growth curves in plant breeding programs has already been used to evaluate dry matter accumulation in garlic plants by Puiatti et al (2018;2020), and the length and width of the fruit of pepper genotypes by Oliveira et al (2021a). In all of these studies, nonlinear QR was efficient in fitting models at different levels and classifying genotypes.…”

Quail growth curve model identity

“…To employ this method, we used a logistic model plus a dummy variable (Puiatti et al, 2020;Safari and Erfani, 2020) to represent each of the eight treatments for each of the three lines. This model, which we refer to as the complete model, is as follows:…”