Site index estimation for clonal eucalypt plantations in Brazil: A modeling approach refined by environmental variables

Scolforo, Henrique Ferraço; McTague, J. P.; Burkhart, Harold E.; Roise, Joseph; Álvares, Clayton Alcarde; Stape, José Luiz

doi:10.1016/j.foreco.2020.118079

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…Conversely, the aridity index exceeded 0.76 in most areas in the central-west region of the study area, denoting water stress throughout the year. Paraiba Valley (southeast of the study area) also has a high aridity index which certainly penalizes Eucalyptus productivity, as evidenced by many recent studies [5,8,14,62]. Our aridity index map ranges likewise to the one presented by Hubbard et al [53].…”

Section: Climate Modelingsupporting

confidence: 74%

“…The strategies used on productivity zoning should span multiple spatial scales and require a sound mechanistic understanding of the interactions between tree resource use dynamics over space and time. In Eucalyptus plantations, the use of ecophysiological and statistical models that incorporate climate variables such as rainfall or soil water deficit, adjusted with appropriate regionwide trials, are great examples to predict forest yield levels in target regions [13,14]. These studies have provided satisfactory results, but once based on experimental data generally scarcer, such approaches cannot perform distribution and probability modeling of the variable to be predicted.…”

Section: Introductionmentioning

confidence: 99%

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Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Álvares

Cegatta

Scolforo

et al. 2023

Forests

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Brazil is one of the world’s wood short-fiber producers, cultivating 7.5 million hectares of eucalypt trees. Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands. Spatial data science (DS) and machine learning (ML) provide powerful approaches to make the best use of the large datasets available today. Agriculture has made great use of these approaches, and in this paper, we explore how forestry can benefit as well. We hypothesized that both DS and ML techniques can be used to improve Eucalyptus productivity zoning based on multiple operational datasets of tree growth and environment. Based on more than 12,000 permanent forest inventory plots of commercial Eucalyptus plantations and the climate, soil, and altitude variables associated with them, a supervised ML approach was adjusted to model the forest plantation productivity. A multi-tuning of the decision-tree (DT) algorithm hyperparameters was prepared to yield 450 DT models, with a better one delivering an RMSE of 53.5 m3 ha−1, split in 35 terminal nodes, here interpreted as Eucalyptus productivity zones. The DT model showed an optimum performance index of 0.83, a coefficient of determination of 0.91, a root mean squared error of 12.3 m3 ha−1, and a mean absolute percentage error only of 3.1% in predicting the testing dataset throughout the study area. The DT rule set was interpreted in a user-friendly table and was prepared to classify any location within the study area in each one of the 35 productivity zones based on the required environment variables of the DT algorithm. The high quality of the model obtained made it possible to spatialize the DT rules, providing a reliable cartographic visualization of the probability levels of true Eucalyptus productivity for a huge region of forest-based industries in Brazil. These data-science techniques also provided a yield gap analysis using a very down-to-earth approach. We estimated a yield gap by an amount of 4.2 × 107 m3, representing a few more than 113,000 ha, or 15% of the current forest base. This is the amount of avoided area expansion to accumulate the same wood stock in case the productivity is raised to the attainable level in each zone. This present study provided deeper analysis and reproducible tools to manage forest assets sustainably.

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Section: Climate Modelingsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Álvares

Cegatta

Scolforo

et al. 2023

Forests

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“…Forest growth is commonly described as a function based on the availability of resources such as water, light, and nutrients, the proportion of resources captured by trees, and the efficiency with which trees use resources to fix carbon dioxide [22]. Among the available resources, water is one of the factors that most strongly affects tree growth [23,24], which is consistent with the results found in this study as the highest carbon stocks were measured at the site with greater water availability (Table 1) (Figure 6). Furthermore, E. urophylla had higher carbon stocks (90.1 Mg C ha −1 ) than E. grandis × E. camaldulensis (72.2 Mg C ha −1 ), demonstrating the greater efficiency of this genotype in utilizing the available resources at the site for carbon fixation (Table 1).…”

Section: Discussionmentioning

confidence: 99%

Fine Root Density Dynamics and Carbon Stock of Eucalyptus spp.: Interplay of Age, Genotype, and Edaphoclimatic Conditions

Basílio,

Campoe,

Queiroz

et al. 2024

Plants

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Roots play a fundamental role in forest ecosystems, but obtaining samples from deep layers remains a challenging process due to the methodological and financial efforts required. In our quest to understand the dynamics of Eucalyptus roots, we raise three fundamental questions. First, we inquire about the average extent of the roots of two contrasting Eucalyptus genotypes. Next, we explore the factors that directly influence the growth and depth of these roots, addressing elements such as soil type, climate, and water availability. Lastly, we investigate how the variation in Eucalyptus species may impact root growth patterns, biomass, and carbon stock. In this study, we observed that the maximum root depth increased by an average of 20% when genotypes were grown on sites with higher water availability (wet site). E. urophylla stands had a higher biomass and carbon stock (5.7 Mg C ha−1) of fine roots when cultivated on dry sites (annual rainfall~727 mm) than the wet sites (annual rainfall~1590 mm). In E. grandis × E. camaldulensis stands, no significant differences were observed in the stock of fine root biomass (3.2 Mg C ha−1) between the studied environments. Our results demonstrated that genotypes with greater drought tolerance (E. grandis × E. camaldulensis) tend to maintain higher stocks of fine root biomass (3.2–6.3 Mg ha−1) compared to those classified as plastic (E. urophylla), regardless of the edaphoclimatic conditions of the cultivation site. Finally, our research helps understand how Eucalyptus trees adapt to their environment, aiding sustainable forest management and climate change mitigation. We also provide a practical tool to estimate underground biomass, assisting forest managers and policymakers in ensuring long-term forest sustainability.

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“…Mohammadi et al [5] also predicted oriental beech productivity, e.g., stand volume using MLR and RT techniques, and RT outperformed with an R 2 of 0.67 (percentage RMSE = 30%). Some other statistical approaches have also been implemented to model forest productivity, such as the complementary methodological approach [95], random forest analysis [14,96,97], Chapman-Richards model [83,98], and linear mixed effects models [99].…”

Section: Predicting and Mapping Sdhmentioning

confidence: 99%

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

Yener,

Guvendi

2023

Sustainability

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The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.

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Site index estimation for clonal eucalypt plantations in Brazil: A modeling approach refined by environmental variables

Cited by 14 publications

References 23 publications

Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Decision-Tree Application to Predict and Spatialize the Wood Productivity Probabilities of Eucalyptus Plantations

Fine Root Density Dynamics and Carbon Stock of Eucalyptus spp.: Interplay of Age, Genotype, and Edaphoclimatic Conditions

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

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