Browsing by Author "Baselly-Villanueva, Juan Rodrigo"

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Configuration of the Deep Neural Network Hyperparameters for the Hypsometric Modeling of the Guazuma crinita Mart. in the Peruvian Amazon.
(Multidisciplinary Digital Publishing Institute, 2022-04) Goycochea Casas, Gianmarco; Elera Gonzales, Duberli Geomar; Baselly-Villanueva, Juan Rodrigo; Pereira Fardin, Leonardo; Garcia Leite, Hélio
Abstract: 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.
Estimating the site quality of Cinchona pubescens (Rubiaceae) in La Palma montane forest, province of Chota, Peru.
(Fundacion Miguel Lillo, 2023-12) Rufasto-Peralta, Yennifer L.; Baselly-Villanueva, Juan Rodrigo; Alva-Mendoza, Denisse M.; Seminario-Cunya, Alejandro; Gonzales Elera, Duberli Geomar; Villena-Velásquez, Jim J.
The genus Cinchona L. (Rubiaceae) has 23 species, of which 19 are distributed in Peru. Although it is a very important genus worldwide, its habitats are being degraded at an accelerated rate. No research on the site quality of these species has been conducted, making it difficult to devise habitat recovery plans. The objective of the research was to estimate the site quality of Cinchona pubescens Vahl., in La Palma montane forest, located in the district and province of Chota, Cajamarca region, Peru. Three circular plots of 500 m2 (r =12.6 m) were established, and the total height and circumference at breast height of the trees were measured. An analysis of variance was performed to evaluate the existence of site classes. Climatic, physiographic and edaphic variables were obtained and correlated with tree height to explain their relationship; in addition, a Principal Component Analysis was performed to explain the variability of the studied variables. No statistical difference was detected between the mean heights of the trees, since all the plots presented similar site quality. The edaphoclimatic factors were not correlated with height. However, the Principal Component Analysis showed that edaphic variables had a greater influence on the height of Cinchona pubescens Vahl. than climatic and topographic variables. This species grows in sandy loam soils, with a strongly acid pH and medium to high concentrations of some elements, such as organic matter, P, K and N.
MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon.
(Multidisciplinary Digital Publishing Institute, 2025-01) Baselly-Villanueva, Juan Rodrigo; Fernández-Sandoval, Andrés; Salazar-Hinostroza, Evelin Judith; Cárdenas-Rengifo, Gloria Patricia; Puerta, Ronald; Chuquizuta Trigoso, Tony Steven; Rufasto-Peralta, Yennifer Lisbeth; Vallejos-Torres, Geomar; Goycochea Casas, Gianmarco; Araújo Junior, Carlos Alberto; Quiñónez-Barraza, Gerónimo; Álvarez-Álvarez, Pedro; Garcia Leite, Helio
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.