Examinando por Autor "Ortega Quispe, Kevin Abner"

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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.
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.