Regression analysis is a traditional technique to fit equations and predict tree and forest attributes. However, problems may occur when the data show high dispersion around the mean of the regressed variable, limiting the use of traditional methods such as the Ordinary Least Squares (OLS) estimator. Hence, the objectives were to propose a Quantile Regression (QR) methodology to predict tree growth and yield of a forest plantation without using the site index and compare it with the predictive accuracy of the Clutter model. The data came from clonal plantations of Eucalyptus grandis x E. urophylla located in the north of Bahia state (Brazil), and age ranged from 20 to 89-months-old. The set of permanent plots was divided into 50% for model training and the remaining for validation. The volume prediction methods, Clutter and QR, were evaluated using scatter plots of the relative error and graphs of the observed versus predicted volumes, as well as by the following statistics: mean prediction bias in percentage (BIAS %) and root mean square error in percentage (RMSE %). The volume predictions by QR resulted in higher accuracy compared to those obtained by the Clutter model. The QR demonstrated efficiency to predict volumes for different ages of eucalyptus plantations and can also be adapted to other forest areas and species worldwide.

回归分析是一种适合方程式并预测树木和森林属性的传统技术。但是，当数据在回归变量的平均值附近表现出高度分散性时，可能会出现问题，从而限制了使用传统方法（如普通最小二乘（OLS）估计器）的限制。因此，目标是提出一种分位数回归（QR）方法，以在不使用站点索引的情况下预测森林种植的树木生长和产量，并将其与Clutter模型的预测精度进行比较。数据来自桉树x尾叶桉的无性系位于巴伊亚州（巴西）北部，年龄在20到89个月大之间。永久样地集分为50％用于模型训练，其余用于验证。使用相对误差的散点图和观测体积与预测体积的关系图以及以下统计数据，评估了体积预测方法（杂波和QR），以及以下统计数据：百分比的平均预测偏差（BIAS％）和均方根误差百分比（RMSE％）。通过QR进行的体积预测与通过Clutter模型获得的预测相比，具有更高的准确性。QR证明了预测不同年龄桉树人工林产量的效率，并且还可以适应全球其他森林地区和物种。