DOI: 10.5061/dryad.5x69p8dhk
title: 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

description: 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.
description: # 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 Dataset DOI:
 [10.5061/dryad.5x69p8dhk](10.5061/dryad.5x69p8dhk) ## Description of the
 data and file structure **Data_lopes_et_al_2025.zip** Experimental dataset
 with raw data for *Eucalyptus* and *Pinus* species, and upscaling dataset
 with processed data referring to commercial plantation areas in Brazil,
 using 2022 as the base year. ### Files and variables #### File:
 Data_lopes_et_al_2025.zip **Description:** Experimental dataset with raw
 data for *Eucalyptus* and *Pinus* species, and upscaling dataset with
 processed data referring to commercial plantation areas in Brazil, using
 2022 as the base year. **1.   Experimental Dataset** **1.1 File list**
 RAW_DATA_BEF_EUCALYPTUS.xlsx RAW_DATA_BEF_PINUS.xlsx
 RAW_DATA_R_EUCALYPTUS.xlsx RAW_DATA_R_PINUS.xlsx **1.2 File description**
 The experimental dataset consists of four Excel files containing raw data
 used to explore and model Biomass Expansion Factors (BEF) and
 Root-to-Shoot Ratios (R) across different species, age classes, and
 climatic conditions. These data were provided by the SilviCarbo project
 (Discussion and Action Forum Related to Carbon Storage and Sequestration
 in Brazilian Forestry), coordinated by the Forestry Science and Research
 Institute (IPEF)
 [https://www.ipef.br/techs/en](https://www.ipef.br/techs/en), with support
 from 10 affiliated forestry companies. These data include individual
 tree-level measurements. The variables are defined as follows: * BEF
 (Biomass Expansion Factor): Factor for estimating total aboveground
 biomass from stem biomass (dimensionless). * R (Root-to-Shoot Ratio):
 Ratio of belowground to aboveground biomass (dimensionless). * dbh
 (Diameter at Breast Height): Stem diameter measured at 1.30 m aboveground
 (in centimeters, cm). * Ht (Total Height): Vertical distance from the
 ground to the top of the tree (in meters, m). * S (Slenderness degree):
 Morphometric characteristic calculated as Ht/dbh (dimensionless). * SP
 (Species): Dummy variable indicating *Pinus* species (1–*Pinus caribaea*
 var. *hondurensis* and 0–*Pinus taeda*). * AC (Age Class): Dummy variable
 indicating age class for *Eucalyptus* trees used in R modeling (1 for
 trees aged ≤ 3 years, 0 for trees aged &gt; 3 years). * AC (Age Class):
 Categorical variable representing age classes for *Pinus* sp, used in BEF
 modeling. * Age: tree age (in years). * T (Average Temperature): Mean
 annual air temperature at the sampling site (in degrees Celsius, °C). * P
 (Precipitation): Mean annual precipitation at the sampling site (in
 millimeters, mm). * TC (Temperature Class): Categorical variable
 representing classes of mean annual temperature. * Köppen (Köppen Climate
 Classification): Climate classification based on the Köppen system
 (Alvares et al., 2013). **2.   Upscaling Dataset** **2.1 File list**
 Brazil_eucalyptus_commercial_scale.xlsx Brazil_pinus_commercial_scale.xlsx
 **2.2 File description** The *upscalin*g dataset comprises *Eucalyptus*
 and *Pinus* plantation areas in Brazil at the commercial scale, as of
 2022, based on a spatial dataset provided by Suzano SA. The datasets
 include the following variables: * UF: Brazilian state where the
 plantation is located. * BEF_paper_map: Biomass Expansion Factor estimated
 using the models developed in this study, extrapolated to the commercial
 scale. * R_paper_map: Root-to-Shoot Ratio estimated using the models
 developed in this study, extrapolated to the commercial scale. * BEF_MCTI:
 Default BEF value defined by the Brazilian Ministry of Science,
 Technology, and Innovation (MCTI, 2004). * R_MCTI: Default R value defined
 by the Brazilian Ministry of Science, Technology, and Innovation (MCTI,
 2004). * diff_BEF and diff_R: Relative difference (in percentage) between
 the default values provided by MCTI (2004) and the values predicted by the
 models developed in this study, for BEF and R, respectively.\ These
 differences are calculated using Equation 13 from the paper:
 ![](data:image/png;base64,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) Where: *yd* = default value from MCTI (2004). *yp* = value predicted by the model. 
 
 **References** Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes
 Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate
 classification map for Brazil. *Meteorologische Zeitschrift*, 22(6),
 711–728.
 [https://doi.org/10.1127/0941-2948/2013/0507](https://doi.org/10.1127/0941-2948/2013/0507) MCTI. (2004). *Comunicação nacional inicial do Brasil à Convenção-Quadro das Nações Unidas sobre Mudança do Clima: Vol. 1a*. [https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/sirene/publicacoes/comunicacoes-nacionais-do-brasil-a-unfccc](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/sirene/publicacoes/comunicacoes-nacionais-do-brasil-a-unfccc)