Abstract Aqueous smoke extract (ASE) is a solution produced by condensing smoke from controlled biomass combustion, with a chemical composition highly dependent on factors such as temperature, moisture, and plant material type. This study aimed to characterize the chemical profile of Eucalyptus grandis ASE using gas chromatography-mass spectrometry (GC-MS). Extraction was performed using hexane, chloroform, and methanol, identifying compounds via the WILLEY library. Analysis revealed 36 compounds in hexane, 37 in chloroform, and 2 in methanol. The predominant substances identified were furfural (15.85%), hentetracontan-1-ol (39.78%), and methoxy(phenyl)methanone oxime (95.18%). The findings underscore the extract’s chemical complexity and its technical potential for agricultural applications. However, the study emphasizes the necessity of defining precise, safe, and effective concentrations, as high levels of the extract may lead to significant toxic effects, requiring careful management in practical use.
Abstract Accurate stem volume prediction is essential for sustainable management of planted forests in ecologically sensitive regions. This study aimed to evaluate and compare Artificial Neural Networks (ANNs) and classical volume models for predicting stem volume of Eucalyptus spp. in an unmanaged stand within the Cerrado–Caatinga ecotone of the Jalapão region, Brazil. A dataset of 134 felled trees, with volumes obtained by the Smalian method, was used to train three ANN architectures (14–3–1) and fit eight classical models. Classical models produced the most accurate predictions, particularly Näslund (R² = 0.93) and Polynomial (R² = 0.93). Among ANNs, ANN 3 achieved the highest performance (R² = 0.90). Paired t-tests showed ANN 1 was statistically equivalent to Näslund and Polynomial, confirming a small practical performance gap. Both approaches are complementary; model selection should consider data availability, interpretability, and management objectives.
Abstract The increasing availability of organic residues from agricultural and industrial activities in southern Tocantins has led to their increased use in substrate formulations for forest seedling production. This study evaluated the effect of different substrates and propagation methods on the quality of Corymbia citriodora seedlings. The experiment was conducted in a nursery using a randomized-block design with four blocks, ten substrate treatments, and two propagation methods (transplanting and direct seeding). The substrates consisted of bird litter, rice husk charcoal, boiler ash, and slaughterhouse residue, a typical Cerrado Plintossolo, and a commercial substrate. The treatment composed of 40 % soil, 30 % slaughterhouse residue and 30 % bird litter yielded the best seedling growth and quality. It is concluded that animal-origin organic compounds are the most suitable for alternative substrate formulation for Corymbia citriodora seedling production under Cerrado conditions.
Abstract Eucalyptus plays a key role in Brazil’s forest sector, yet studies in northeastern Brazil remain scarce, particularly on stand volume estimation. This study evaluated different integration levels between unmanned aerial vehicle (UAV) data and traditional forest inventory (TFI) in a 10-year-old Eucalyptus spp. stand in Palmeira do Piauí, Brazil. Twelve plots were sampled, and UAV-RGB imagery was used to generate an orthomosaic and canopy height model. Diameter-height models were fitted using ordinary least squares and linear mixed-effects (LME). UAV-derived tree heights matched TFI measurements, with a difference of 0.9 m. Automated tree detection underestimated tree density, indicating that improvements in detection algorithms are required under the evaluated canopy conditions. Combining LME-based diameter model with visually interpreted tree counts, UAV-derived stand volume estimates (63.5-75.6 m³ ha⁻¹) were statistically equivalent to TFI (79.3 m³ ha⁻¹). These findings support use of low-cost UAV-RGB data with LME modeling for reliable stand volume estimation.
Abstract We evaluated the composition and vertical distribution of epiphytic orchids in floodplain forests under intensive management for açaí production at the mouth of the Tocantins River, in the Brazilian Amazon. All host trees with a diameter at breast height ≥ 10 cm were sampled within a one-hectare area. We recorded 316 individuals of epiphytic orchids, belonging to 13 species and 12 genera. The most abundant species were Lockhartia imbricata (88 individuals) and Dimerandra emarginata (74). Seventeen trees from five families, eight genera, and eight species served as structural support for orchids. Hevea brasiliensis was the main host tree, with eight individuals supporting an average of 16 individual orchids each. The crown had the greatest number and abundance of orchid species, and 54% of the species occurred exclusively in this stratum. Our results are important for understanding the ecology of orchids inhabiting floodplain forests at river mouths impacted by anthropogenic activities.
