Data from: Improved estimates of biomass expansion factors and root-to-shoot ratios: An approach for different forest types across a climatic gradient in Brazil

Advancements in the current state of the art of the key drivers of biomass
expansion factor (BEF) and the root-to-shoot ratio (R) are crucial for
producing accurate information on forest biomass and carbon stocks. Hence,
we compiled a nationally representative dataset encompassing diverse tree
growth stages and climatic gradients. In this study, we propose models to
improve BEF and R estimates at the tree level for Eucalyptus and Pinus
plantations in Brazil. In general, the BEF values were more representative
(91.7%) in the database than the R values (8.3%) due to the high cost of
collecting coarse roots. Regarding genera, Eucalyptus was more extensively
sampled (89.9%) than Pinus (10.1%), reflecting the predominance of
Eucalyptus as the most widely planted genus in Brazil. The average BEF and
R values calculated in this study were 1.16 and 0.22, respectively, for
Eucalyptus spp. and 1.22 and 0.31, respectively, for Pinus spp. In
predicting the BEFs, the random effects in the linear mixed model that
significantly captured the variations in Eucalyptus and Pinus spp. were
temperature and age class, respectively. The fixed effects for Eucalyptus
spp. included diameter, height, and age, while for Pinus spp., they were
the Köppen climate classification, species, slenderness degree, and age. R
estimates were mainly influenced by precipitation and age for both genera,
with slenderness and diameter specifically affecting Eucalyptus spp. and
height being a driving factor for Pinus spp. Our findings discourage the
use of fixed or default values ​​for BEF and R across locations with
different climates and growing conditions to reduce uncertainties in
carbon accounting and greenhouse gas inventories.

当前关于生物量扩展因子（biomass expansion factor, BEF）与根冠比（root-to-shoot ratio, R）核心驱动因素的研究进展，对精准获取森林生物量与碳储量数据至关重要。为此，我们构建了一套具有全国代表性的数据集，涵盖多样的树木生长阶段与气候梯度。本研究针对巴西的桉树（Eucalyptus）与松属（Pinus）人工林，提出了用于优化单木水平BEF与R估算的模型。总体而言，由于采集粗根的成本高昂，数据库中BEF数据占比（91.7%）远高于R数据（8.3%）。按属类划分，桉树的采样占比（89.9%）显著高于松属（10.1%），这与巴西境内桉树人工林种植最为广泛的现状相符。本研究计算得到的平均BEF与R值：桉树属（Eucalyptus spp.）分别为1.16与0.22，松属（Pinus spp.）分别为1.22与0.31。在BEF预测方面，线性混合模型（linear mixed model）中的随机效应分别为温度（对桉树属）与龄级（对松属），二者均有效捕捉了两类树种的变异。桉树属的固定效应包括直径、树高与年龄；松属的固定效应则涵盖柯本气候分类（Köppen climate classification）、树种、修长度与年龄。对于两类树种的R估算，主要影响因素均为降水量与年龄；其中修长度与直径仅对桉树属R估算存在影响，而树高则是松属R估算的驱动因子。本研究结果表明，不应在不同气候与生长条件的区域统一使用固定值或默认值估算BEF与R，以此降低碳核算与温室气体清单编制中的不确定性。