Forest plantations can be established in order to restore degraded areas. Acoustic tomography, which is of increasing importance in forest management, was used in the present study to obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon. The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration. Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation. The indicator variables and explanatory variables were compared and evaluated using correlation and principal component analysis. Wood deterioration was modelled using stepwise regression. The indicator variables differed significantly between plantations and were mainly correlated with the biometric variables (age and diameter at breast height). The models explained 81% of the variability of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations in the Peruvian Amazon.

This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R² values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m³ha⁻¹ and the sawn lumber volume 333.6 m³ha⁻¹. The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.

Ulcumano, which is native to South America, is an important conifer in Peru. Molecular studies are scarce, limiting modern breeding and appropriate conservation activities. Currently, molecular markers are widely employed to explore genetic structure and diversity parameters of plant species in a fast and precise manner. The objective of this study was to analyze the genetic diversity and population structure of ulcumano in Peru by using DNA-based molecular markers. Nine Randomly Amplified Polymorphic DNA (RAPD) markers were used, while 95 individuals of ulcumano were sampled from three departments of Peru. A total of 265 DNA fragments were manually scored, but 247 of them were kept after removing the non-polymorphic markers. Genetic distances were calculated using R software based on Provesti´s coefficient. A dendrogram was obtained using the UPGMA clustering algorithm, showing no clear clustering. The principal coordinate analysis agreed with two population structure analyses, demonstrating that ulcumano is contained within two clusters, (i) Junín + Pasco, and (ii) Cajamarca, while very few individuals are intermixed. Genetic diversity parameters were estimated considering the two groups (populations) identified by STRUCTURE software. Nei's genetic diversity estimate varied between 0.22 and 0.28, while Shannon index ranged from 3.43 to 4.16. Population divergence (Fst) between the two clusters revealed low genetic differentiation (0.064). AMOVA analysis revealed that 87.31 and 12.69% of the total genetic variation were found within populations and between individuals, respectively. To the best of our knowledge, this is the first molecular study in ulcumano in Peru, and provides valuable information for the genetic improvement and sustainable management of this conifer in the country.

Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems.

Neltuma pallida (algarrobo) is a keystone species of the Peruvian dry forest whose persistence is threatened by overexploitation and habitat degradation, making its conservation and genetic improvement a national priority. This study aimed to identify outstanding phenotypes of N. pallida through phenotypic characterization in the regions of Piura and Tumbes in northern Peru. A stratified random sampling design was applied, establishing forest plots in 13 localities and evaluating 631 adult individuals. Dendrometric and phenotypic traits were recorded together with physiographic, climatic, and edaphic variables obtained from soil analyses and geographic information systems. Phenotypic differentiation among populations was assessed using the index $P_{ST}$, which quantifies between- and within-population variance components. High morphological variability was detected across populations, with significant differences in tree height, diameter, and fruit production, largely explained by environmental heterogeneity, particularly soil fertility and organic matter. The $P_{ST}$ values, ranging from 0.83 to 0.98, revealed strong phenotypic divergence among populations, suggesting adaptive differentiation rather than neutral variation. Eight superior individuals were identified as potential candidates for inclusion in a germplasm bank to support breeding and restoration programs. This study provides a scientific basis for the sustainable management and conservation of N. pallida in the arid ecosystems.