Artículos de investigación en revistas indizadas en Web of Science, Scopus y Scielo
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Item The floriculture as an alternative crop: Descriptive analysis, artificial intelligence modeling, scenario analysis and economic analysis(Scientia Agropecuaria, 2025) Coaguila-Rodriguez, Peter; Pocomucha-Poma, Vicente Serapio; Cerna-Cueva, Alberto FrancoFloriculture is a sector of growing global importance, contributing to employment generation, income creation, and the promotion of biodiversity and sustainability. This study aimed to identify the factors influencing the adoption of floriculture as an alternative crop in the province of Leoncio Prado, Peru, and to assess its economic viability. A total of 269 farmers were surveyed, analyzing attitudes, land suitability, and socioeconomic and environmental factors. Influential factors were identified using descriptive analysis, chi-square tests, and logistic regression (p < 0.1). Additionally, multiple machine learning algorithms (Decision Trees, Logistic Regression, KNN, SVM, Ensemble, Neural Networks, Naive Bayes) with cross-validation (k = 5) and AUC metrics were employed to model adoption intentions. Scenarios were developed to increase the willingness to adopt floriculture, and an economic analysis of eight tropical species (Red Ginger, Anthurium, Emperor's Staff, Heliconia, Gardenia, Parrot's Beak, Golden Heliconias, Maracas) was conducted. The results reveal that willingness to change crops, participation in awareness campaigns, allocation of areas for conservation, and cost control are key factors. The neural network model achieved an AUC of 83.3%, and improved scenarios indicate that adoption could increase by up to 11.32%. Red Ginger demonstrated high profitability (NPV S/10428; IRR 51%; PBP 0.7 years). In conclusion, floriculture represents an economically and environmentally viable alternative that contributes to agricultural diversification and sustainability. © 2025 Universidad Nacional de Trujillo. All rights reserved.Item MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon(Forests, 2025) Baselly-Villanueva, Juan Rodrigo; Fernández-Sandoval, Andrés; Salazar Hinostroza, Evelin Judith; Cárdenas Rengifo, Gloria Patricia; Puerta, Ronald; Trigoso, Tony Steven Chuquizuta; Rufasto-Peralta, Yennifer Lisbeth; Vallejos-Torres, Geomar; Casas, Gianmarco Goycochea; Araújo Junior, Carlos Alberto; Quiñónez-Barraza, Gerónimo; Álvarez-Álvarez, Pedro; Leite, Helio GarcíaThis 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. © 2025 by the authors.Item Native Microbial Consortia: A Sustainable Strategy for Improving the Quality of Forest Seedlings in the Peruvian Amazon(Forests, 2025) Amaringo-Cordova, Luiz Paulo; Mori-Montero, Cesar; Padilla-Castro, Cesar; Ocaña-Reyes, Jimmy A.; Riveros-Lizana, Christian; Camacho-Villalobos, Alina Alexandra; Solórzano-Acosta, RichardForest plantations represent an alternative to reduce timber extraction pressure in the Amazonian forests. In order to tolerate the hostile field conditions of deforested areas, high-quality seedlings are required. This study aimed to find the optimal dose of a native microbial consortium (NMC), which enhances seedling quality indicators, in three forest species at nursery phase. A completely randomized design (3 × 5) was used. Factor 1: Bolaina blanca (Guazuma crinita Mart.), Capirona (Calycophyllum spruceanum Benth. Hook. f.), and Marupa (Simarouba amara Aubl.). Factor 2: Incremental doses of 0, 160, 320, 480, and 640 mL NMC per plant. The nursery survival (%), robustness index, root height/length ratio, shoot–root index, Dickson Quality Index (DQI), Nitrogen (%), Phosphorus (%), and Potassium (%) content in tissues were analyzed. Statistical analyses consisted of two-way ANOVA per variable and correlation analysis. The results indicated that increasing doses of NMC did not improve nursery survival for any species; did not decrease the robustness index, plant height/root length ratio, or the shoot–root index for any species; and did not increase the DQI, P%, or K% for any species; however, they did increase the N% for all species. In conclusion, the incremental dose of 160 mL was chosen for increasing the N% without affecting nursery survival. © 2025 by the authors.
