Examinando por Autor "Ccopi Trucios, Dennis"

Mostrando 1 - 7 de 7

An approach to the impact of weather variables on the growth of Polylepis species in the central Andes of Peru
(University of Forestry, 2025-01-10) Ortega Quispe, Kevin Abner; Cordova Torres, Betty; Molina Damas, Meliza; Oscanoa Ramos, Judith; Enriquez Pinedo, Lucía Carolina; Flores Torres, Itala; Ccopi Trucios, Dennis
The Polylepis genus, endemic to the South American Andes, faces significant threats due to environmental variations, which jeopardize its growth and survival. This situation underscores the urgent need to develop conservation strategies. The present research assesses the influence of meteorological variables, such as temperature and humidity, on the growth and adaptation of various Polylepis species in the central Peruvian Andes, aiming to optimize reforestation and sustainable management practices. The study was conducted in experimental plots at the Santa Ana Agricultural Station in Junín, Peru, where Polylepis saplings, obtained from different localities, were planted. Over two years, phenotypic variables (height and diameter) and meteorological variables (precipitation, humidity, temperature, and wind speed) were monitored to evaluate the relationship between environmental conditions and plant development. The results showed that high humidity negatively affected all species, however wind speed appears to promote plant growth by creating an ideal microclimate that reduces soil moisture. Precipitation and maximum temperature had limited impact, indicating relative resilience to these factors. It should be noted that the species from Huancavelica and Yauyos have been adapting better to local conditions compared to those from Cerro de Pasco, which are more sensitive to humidity. These findings highlight the importance of considering wind speed and humidity in reforestation planning to improve the adaptability of Polylepis species. We conclude that humidity is the most decisive meteorological factor for the growth of Polylepis under specific conditions, emphasizing its relevance in planning conservation and reforestation strategies in the Peruvian Andes.
Analysis of soil quality through aerial biomass contribution of three forest species in relict high Andean forests of Peru
(Malaysian Society of Soil Science, 2024-05-17) Zanabria Cáceres, Ysaias Timoteo; Cordova Torres, Betty; Clemente Archi, Gelly; Zanabria Mallqui, Rosario Magaly; Enriquez Pinedo, Lucia Carolina; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner
The biomass that accumulates on the forest floor and its subsequent decomposition play an important role in maintaining the productivity of different terrestrial ecosystems by constituting the main nutrient flow to the soil. The objective of the study focused on analyzing the nutrient contribution to the soil derived from the aboveground biomass of three native forest species in relict forests of the Central Peruvian Sierra with socioeconomic and environmental relevance. Using random delineation methods, soil samples were collected at 20-30 cm depth, which were subjected to physical, chemical, and biological analyses, developing the determination of a Soil Quality Index (SQI). The results highlight that forests of Polylepis racemosa and Alnus acuminata significantly exhibit a higher SQI, with values of 0.66 and 0.58, respectively, compared to Escallonia resinosa, with the forestless system being of lower quality with an SQI of 0.28. The relict forests, Dorado, Colpar, and Talhuis, presented the highest SQIs (0.53, 0.52, and 0.48), while Saño obtained the lowest SQI with 0.39, with no significant differences among them. The forests of Polylepis racemosa and Alnus acuminata showed a superior soil structure, higher organic matter content, moisture retention, and microbial biomass compared to other analyzed systems.
Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru
(MDPI, 2025-03-06) Enriquez Pinedo, Lucia Carolina; Ortega Quispe, Kevin Abner; Ccopi Trucios, Dennis; Rios Chavarria, Claudia Sofía; Urquizo Barrera, Julio; Patricio Rosales, Solanch Rosy; Alejandro Mendez, Lidiana Rene; Oliva Cruz, Manuel; Barboza Castillo, Elgar; Pizarro Carcausto , Samuel Edwin
Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (-0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices.
Efficiency of a compound parabolic collector for domestic hot water production using the F- chart method
(International Hellenic University School of Science and Technology, 2024-06-01) Ortega Quispe, Kevin Abner; Huari Vila, Oscar Paul; Ccopi Trucios, Dennis; Lozano Povis, Arlitt Amy; Enriquez Pinedo, Lucia Carolina; Cordova Torres, Betty
Among solar energy technologies, differences exist in terms of costs, performance, and environmental sustainability. Flatplate solar collectors, solar towers, and parabolic dish systems offer high thermal efficiency and versatility, but they may be more costly and bulky compared to other collector models. This study focused on evaluating the efficiency of a cylindrical parabolic collector (CPC) for the production of domestic hot water in a high Andean region of Peru, using the F-Chart method. Its performance was estimated considering the energy demand for hot water in a single-family home with four occupants, in accordance with national regulations and international recommendations. Additionally, the collector area, water temperature, and incident solar radiation were determined based on meteorological data obtained using the PVsyst software. On the other hand, the F-Chart methodology was employed to find the dimensionless factors X and Y of the CPC collector, which allowed estimating the solar fraction factor and the monthly useful energy that can be provided by the designed CPC system. The results showed that, during months of maximum solar radiation, the CPC is capable of satisfying between 129% and 144% of the energy demand for hot water. This indicates that there is a surplus of usable solar energy in the collector during the summer, while in autumn and winter, the solar contribution balances and slightly exceeds the demand. CPC can significantly contribute to the development of high Andean areas by improving quality of life, reducing costs, and promoting environmental sustainability compared to other available technologies.
Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
(MDPI, 2024-10-06) Urquizo Barrera, Julio Cesar; Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Patricio Rosales, Solanch; Passuni Huayta, Jorge; Figueroa Venegas, Deyanira; Enriquez Pinedo, Lucia; Ore Aquino, Zoila; Pizarro Carcausto, Samuel
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR
(Elsevier B.V., 2024-12-22) Enriquez Pinedo, Lucía; Ortega Quispe, Kevin; Ccopi Trucios, Dennis; Urquizo Barrera, Julio; Rios Chavarría, Claudia; Pizarro Carcausto, Samuel; Matos Calderon, Diana; Patricio Rosales, Solanch; Rodríguez Cerrón, Mauro; Ore Aquino, Zoila; Paz Monge, Michel; Castañeda Tinco, Italo
The lack of precise methods for estimating forest biomass results in both economic losses and incorrect decisions in the management of forest plantations. In response to this issue, this study evaluated the effectiveness of using the DJI Zenmuse L1 LiDAR, mounted on a DJI Matrice 300 RTK UAV, to provide three-dimensional measurements of canopy structure and estimate the aboveground biomass of Eucalyptus globulus. Various LiDAR metrics were employed alongside field measurements to calibrate predictive models using multiple regression and machine learning algorithms. The results at the individual tree level show that RF is the most accurate model, with a coefficient of determination (R²) of 0.76 in the training set and 0.66 in the test set, outperforming Elastic Net (R² of 0.58 and 0.57, respectively). At the plot level, a multiple regression model achieved an R² of 0.647, highlighting LiDAR-derived metrics as key predictors. The findings revealed that the combination of LiDAR with advanced statistical techniques, such as multiple regression and Random Forest, significantly improves the accuracy of biomass estimation, surpassing traditional methods based on allometric equations. Therefore, the use of LiDAR in conjunction with machine learning represents an effective alternative for biomasss estimation, with great potential in such plantations and contribute to more sustainable exploitation of timber resources.
Using UAV images and phenotypic traits to predict potato morphology and yield in Peru
(MDPI, 2024-10-24) Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Rios Chavarria, Claudia; Enriquez Pinedo, Lucia; Patricio Rosales, Solanch; Ore Aquino, Zoila; Casanova Nuñez Melgar, David; Agurto Piñarreta, Alex Iván; Zúñiga López, Luz Noemí; Urquizo Barrera, Julio
Precision agriculture aims to improve crop management using advanced analytical tools.In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral índices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.