The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others.

Tornillo is a forest species of wide distribution in the Peruvian Amazon, its irrational exploitation has generated losses in its diversity. Currently, there is no baseline for the development of conservation strategies due to limited knowledge of the structure genetic in tornillo populations which would allow the implementation of an adequate conservation program for the species. 91 individuals of the species were collected in five departments (Madre de Dios, Loreto, Puno, San Martín and Ucayali). The 5 most polymorphic RAPD primers were selected (OPA02, OPA04, OPA12, OPA18, and OPF05), identifying 96 polymorphic markers. The PIC ranged from 0.24 - 0.31; the AMP was 47.86%. No duplicates were reported. He between populations varied between 0.265 - 0.296; Madre de Dios presented a lower value (0.174). The Shannon index presented the same variation (0.402 - 0.447; 0.262). There is a spatial-genetic correlation (rxy = 0.311; p value < 0.001), specifically a genetic variability is found between departments (PhiPT = 0.256; p value < 0.0001). Loreto and Ucayali are genetically related. At the same time, San Martín and Puno have an origin in Ucayali (Masisea) and San Martín have an origin in Loreto (San Juan Bautista). These results will allow to initiate the bases of a conservation program for the sustainable use of the species.

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.

Aim of study: The loss of forest cover is a global problem that alters ecosystems, contributing to carbon emissions. This study measured the soil organic carbon (SOC) at different soil depths in tropical dry forests of the Huallaga Central in the Peruvian Amazon. Area of study: San Martín Region, Peruvian Amazon. Material and methods: A total of 24 plots of 100 m2 were selected in primary (~200 years), intervened (~50 years since intervention), and deforested forests (10 years ago), with 120 soil samples collected across five depths. Soil texture (hydrometer), bulk density (cylinder method), SOC content, SOC density, and erodibility (K parameter) were calculated. Main results: SOC content in the 0-20 cm soil horizon was 79.5±21.3 t ha-1 for the primary forest, 58.5±11.8 t ha-1 for the intervened forest, and 41.8±10 t ha-1 for the deforested forest. A soil erodibility K of 0.065 was observed for primary forests and 0.076 and 0.093 for intervened and deforested forests. In average, the SOC density obtained in this study was 7.6±5.1 t ha-1 in the primary forest, 6.2±3.6 t ha-1 in the intervened forest, and 4.7±2.7 in the deforested forest. Research highlights: Primary forests had the highest SOC content and SOC density, followed by intervened and deforested forests, while the opposite pattern was found for soil erodibility. These patterns were especially marked in the first 40 cm of soil depth.

El género Cinchona L. (Rubiaceae) agrupa 23 especies, de las cuales 19 se encuentran distribuidas en Perú. Pese a ser un género importante a nivel mundial, sus hábitats están siendo degradados de manera acelerada, sin contar con investigaciones sobre la calidad de sitio de estas especies, dificultando generar planes de recuperación de su hábitat. El objetivo de la investigación fue estimar la calidad de sitio de Cinchona pubescens Vahl., en el bosque montano La Palma, localizado en el distrito y provincia de Chota, región Cajamarca, Perú. Se establecieron tres parcelas circulares de 500 m2 (r =12,6 m), donde se midió la altura total y la circunferencia a la altura del pecho de los árboles. Se realizó el análisis de varianza para evaluar la existencia de clases de sitio. Se obtuvieron variables climáticas, fisiográficas y edáficas que fueron correlacionadas con la altura de los árboles, para explicar su relación; además se realizó un Análisis de Componentes Principales con el fin de explicar la variabilidad de los parámetros en estudio. Se determinó que no existe diferencia estadística entre las alturas medias de los árboles; por cuanto, todas las parcelas presentan similitud de calidad de sitio. Los factores edafoclimáticos no presentaron correlación con la altura. Sin embargo, el Análisis de Componentes Principales, evidenció que las variables edáficas influyen más que las climáticas y topográficas sobre la altura de Cinchona pubescens Vahl. Esta especie crece en suelos francos arenosos, con pH fuertemente ácido y concentraciones medias a altas de algunos elementos como materia orgánica, P, K y N.