Quantile regression of nonlinear models to describe different levels of dry matter accumulation in garlic plants

ABSTRACT: Plant growth analyses are important because they generate information on the demand and necessary care for each development stage of a plant. Nonlinear regression models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile regression can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile regression model for the study of dry matter accumulation in garlic plants (Allium sativum L.) over time, estimating parameters at three different quantiles and classifying each garlic accession according to its growth rate and asymptotic weight. The nonlinear regression model fitted was a Logistic model, and 30 garlic accessions were evaluated. These 30 accessions were divided based on the model with the closest quantile estimates; 12 accessions were classified as of lesser interest for planting, 6 were classified as intermediate, and 12 were classified as of greater interest for planting.

摘要：植物生长分析具有重要学术与应用价值，可为植物各发育阶段的养护需求及必要管理措施提供参考依据。非线性回归模型可有效描述生长曲线，因其所包含的参数均具备实际生物学解释意义。但这类模型仅基于条件均值输出分析结果，且在模型拟合过程中易受数据异常值或分布不对称性的影响，引发拟合偏差问题。分位数回归（Quantile regression）则可有效解决上述问题，其支持对不同分位数进行估计，能够生成更为全面且稳健的分析结果。本研究旨在构建非线性分位数回归模型，以分析大蒜（Allium sativum L.）随时间推移的干物质积累动态，同时对三个不同分位数下的模型参数进行估计，并依据各大蒜种质的生长速率与渐近重量完成分类。本次研究采用Logistic模型（Logistic model）作为拟合的非线性回归模型，共评估了30份大蒜种质资源。基于拟合效果最优的分位数估计模型，将30份种质划分为三类：12份被归类为种植价值较低的种质，6份为中等价值种质，剩余12份则为种植价值较高的种质。