Forestry, Artificial Intelligence, Management Science and Operations Research
28
Scopus Publications
Scopus Publications
Forwarder Machine Performance in Eucalyptus Forests in Brazil with Different Productivity Levels: An Analysis of Production Costs Francisco Ferreira, Luís Freitas, Elton Leite, Márcio Silva, Sérgio Santos, et al. Forests, 2025 The objective of this study was to evaluate the influence of the mean individual volume per tree (MIV) on the productivity of forwarder machines and the production cost in eucalyptus plantations located in southern Bahia, Brazil. MIV positively influenced the productivity and production costs, promoting a more attractive cost in the latter when the individual volume per tree increased. The machine’s productivity for MIV of 0.13 m3 was 42.06 cubic meters per effective working hour (m3Ewh−1), while the productivity for the MIV of 0.58 m3 reached 60.97 m3Ewh−1, corresponding to an increase of 42.59% between the minimum and maximum MIV classes. The extracted cost (m3) decreased by 30.12% from USD 2.49 to 1.74, respectively, when comparing the minimum and maximum MIV classes. The coefficient of determination obtained in the forwarder productivity modeling was significant (R2 = 92%), indicating the machine’s productivity can be explained by the mean individual volume per tree. The highest forwarder yields in the highest average volume per tree (MIV) classes provided better energy efficiency indices for the machine; that is to say, when the forwarder became more productive, the ratio between fuel consumption per cubic meter of timber harvested decreased, providing better performance for the respective index. There was a difference in extraction costs of USD 147.83 per hectare between the lowest and highest productivity forests (MIV varying from 0.15 to 0.58). The mechanical availability and mean operational efficiency of all forwarders evaluated were above 80%, which contributed to effective machine productivity performance. Maintenance and repairs represented the largest portion of operational costs (33.59%), followed by labor (22.49%), depreciation (14.33%), and fuel (10.11%).
ECONOMIC FEASIBILITY AND MONTE CARLO SIMULATION IN NURSERY FOR NATIVE FOREST SEEDLING PRODUCTION IN THE SOUTHWEST REGION OF BAHIA, BRAZIL Débora Caroline Defensor Benedito, Kemele Cristina Coelho, Adalberto Brito de Novaes, Vânia Beatriz Cipriani, Natália Saudade de Aguiar, et al. Floresta, 2025 Forest seedling nurseries are regarded as projects that require high initial investment, therefore, it is necessary to assess the economic viability and the risks that may influence results. This study aimed to analyze the viability of implementing a forest nursery of native species seedlings in Vitória da Conquista, BA, Brazil, aiming at meeting regional demand. The study was carried out by simulating an implementation project of a native seedling nursery, with productive capacity of 415,904 seedlings per year. A planning horizon of 10 years and an interest rate of 8% per year was used to analyze the economic feasibility of the cash flow of the enterprise. The following economic viability indicators were calculated with Microsoft Excel®: Net Present Value (NPV); Internal Rate of Return (IRT); Benefit Cost Ratio (B/CR); Equivalent Periodic Value (EPV); and Average Production Costs (APC). Excel® was also used to perform a Monte Carlo simulation for risk assessment, which consisted of running 10,000 iterations. Input variables were the success rate of seedling production, seedling selling price and workforce cost. NPV was the output variable. The results indicated the viability of all economic indicators calculated. Monte Carlo simulation indicated a profitable probability of 82.46%. Economic evaluation and risk assessment pointed to the viability of establishing a nursery of native forest species seedlings in southwest Bahia.
Artificial neural networks and regression analysis for volume estimation in native species1 Lucas M. Amorim, Elton da S. Leite, Deoclides R. de Souza, Liniker F. da Silva, Carlos R. de Mello, et al. Revista Brasileira De Engenharia Agricola E Ambiental, 2021 Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.
Allometric equations for volume, biomass, and carbon in commercial stems harvested in a managed forest in the southwestern amazon: A case study Flora Magdaline Benitez Romero, Laércio Antônio Gonçalves Jacovine, Sabina Cerruto Ribeiro, Carlos Moreira Miquelino Eleto Torres, Liniker Fernandes da Silva, et al. Forests, 2020 Forests in the southwestern Amazon are rich, diverse, and dense. The region is of high ecological importance, is crucial for conservation and management of natural resources, and contains substantial carbon and biodiversity stocks. Nevertheless, few studies have developed allometric equations for this part of the Amazon, which differs ecologically from the parts of Amazonia where most allometric studies have been done. To fill this gap, we developed allometric equations to estimate the volume, biomass, and carbon in commercial trees with diameter at breast height (DBH) ≥ 50 cm in an area under forest management in the southeastern portion of Brazil’s state of Acre. We applied the Smalian formula to data collected from 223 felled trees in 20 species, and compared multiple linear and nonlinear models. The models used diameter (DBH) measured at 1.30 m height (d), length of the commercial stem (l), basic wood density (p), and carbon content (t), as independent variables. For each dependent variable (volume, biomass, or carbon) we compared models using multiple measures of goodness-of-fit, as well as graphically analyzing residuals. The best fit for estimating aboveground volume of individual stems using diameter (d) and length (l) as variables was obtained with the Spurr model (1952; logarithmic) (root mean square error (RMSE) = 1.637, R² = 0.833, mean absolute deviation (MAD) = 1.059). The best-fit equation for biomass, considering d, l, and p as the explanatory variables, was the Loetsch et al. (1973; logarithmic) model (RMSE = 1.047, R² = 0.855, MAD = 0.609). The best fit equation for carbon was the Loetsch et al. (1973; modified) model, using the explanatory variables d, l, p, and t (RMSE = 0.530, R² = 0.85, MAD = 0.304). Existing allometric equations applied to our study trees performed poorly. We showed that the use of linear and nonlinear allometric equations for volume, biomass, and carbon can reduce the errors and improve the estimation of these metrics for the harvested stems of commercial species in the southwestern Amazon.
Necromass carbon stock in a secondary Atlantic forest fragment in Brazil Paulo Henrique Villanova, Carlos Moreira Miquelino Eleto Torres, Laércio Antônio Gonçalves Jacovine, Carlos Pedro Boechat Soares, Liniker Fernandes da Silva, et al. Forests, 2019 Necromass has a relevant role to play in the carbon stock of forest ecosystems, especially with the increase of tree mortality due to climate change. Despite this importance, its quantification is often neglected in tropical forests. The objective of this study was to quantify the carbon storage in a secondary Atlantic Forest fragment in Viçosa, Minas Gerais, Brazil. Coarse Woody Debris (CWD), standing dead trees (snags), and litter were quantified in twenty 10 m x 50 m plots randomly positioned throughout the forest area (simple random sampling). Data were collected during 2015, from July to December. The CWD and snags volumes were determined by the Smalian method and by allometric equations, respectively. The necromass of these components was estimated by multiplying the volume by the apparent density at each decomposition classes. The litter necromass was estimated by the proportionality method and the average of the extrapolated estimates per hectare. The carbon stock of the three components was quantified by multiplying the necromass and the carbon wood content. The total volume of dead wood, including CWD and snag, was 23.6 ± 0.9 m3 ha−1, being produced mainly by the competition for resources, senescence, and anthropic and climatic disturbances. The total necromass was 16.3 ± 0.4 Mg ha−1. The total carbon stock in necromass was 7.3 ± 0.2 MgC ha−1. The CWD, snag and litter stocked 3.0 ± 0.1, 1.8 ± 0.1, and 2.5 ± 0.1 MgC ha−1, respectively. These results demonstrate that although necromass has a lower carbon stock compared to biomass, neglecting its quantification may lead to underestimation of the carbon balance of forest ecosystems and their potential to mitigate climate change.
Carbon stock growth in a secondary atlantic forest Paulo Henrique Villanova, Carlos Moreira Miquelino Eleto Torres, Laércio Antônio Gonçalves Jacovine, Carlos Pedro Boechat Soares, Liniker Fernandes da Silva, et al. Revista Arvore, 2019
