Examinando por Autor "Carbajal Llosa, Carlos Miguel"

Mostrando 1 - 4 de 4

Evaluation of the Flood Area in the Presence of Climate Change: Ravine La Ronda Case, Ricardo Palma, Peru
(Horizon Research Publishing, 2024-11-13) Perez Campomanes, Giovene; Perez Campomanes, Maria; Carbajal Llosa, Carlos Miguel
In the district of Ricardo Palma, human settlements are located near streams, which are seriously affected during the heavy flooding season (rainy season), which increases due to the presence of the El Niño Southern Oscillation (ENSO) and the global effect of climate change. To get to know the flood zone 09 de Octubre - La Ronda, Ricardo Palma, software was applied to obtain the area of influence to study, and to know 10.5 software was applied to obtain the area of influence to study, and to know the rainfall record of the National Water Service. Meteorology and Hydrology of Perú(SENAMHI) for a continuous period of 27 years of maximum daily rainfall, with the HEC HMS 4.9 software the maximum design flows for different return periods were obtained, and the HEC RAS 6.2 software to obtain the flooding area. To find out the support of the authorities in the presence of the Niño Southern Oscillation (ENSO), and the global effect of climate change, a survey was carried out among the population, obtaining that 14.43% had the appropriate conditions to mitigate the impacts of the flooding due to intense rains, 22.93% received help in the presence of the El Niño phenomenon, and for 20.75%, there was a response from their authorities to the needs of the population in the presence of an emergency, and no changes that benefit the population were distinguished. The maximum design flows were calculated for a return period of 10 and 100 years, which vary between 31.7 m³/s and 61.2 m³/s, and that for a design flow of 61.2 m³/s, the flooding area of 0.25 km² was obtained.
Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery
(MDPI, 2023-06-20) Pizarro Carcausto, Samuel Edwin; Pricope, Narcisa G.; Figueroa Venegas, Deyanira Antonella; Carbajal Llosa, Carlos Miguel; Quispe Huincho, Miriam Rocío; Vera Vilchez, Jesús Emilio; Alejandro Méndez, Lidiana Rene; Achallma Mendoza, Lino; González Tovar, Izamar Estrella; Salazar Coronel, Wilian; Loayza, Hildo; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
Soil management in Lepidium meyenii (maca) monoculture: trends and challenges for small farmers around Lake Chinchaycocha in the Andean highlands of Junin (Peru)
(Frontiers Media S.A., 2025-01-17) Solórzano Acosta, Richard; Chanco, Mirella; Seminario, Martín; Camel Paucar, Vladimir Fernando; Cabello Torres, Rita; Lastra Paucar, Sphyros Roomel Luciano; Arias Arredondo, Alberto Gilmer; Verástegui Martínez, Patricia; Quispe Matos, Kenyi Rolando; Carbajal Llosa, Carlos Miguel; Cuevas Gimenez, Juan Pablo; Cruz Luis, Juancarlos Alejandro; Turín Canchaya, Cecilia Claudia
Introduction: Monoculture is a significant concern due to its negative impact on soil quality, resource productivity, and agricultural sustainability, particularly in vulnerable communities. This research aims to evaluate high Andean soil management for maca monoculture. Materials and methods: To this end, interviews were conducted with maca farmers adjacent to Lake Chinchaycocha. The effect on soil quality was evaluated based on principal component analysis (PCA), weighted soil quality index (SQIw), and physico-chemical characteristics. Results: The results indicated differences between farmers in agronomic management, monoculture period (from 5 to 9 years), and fallow time (up to 10 years in the best cases). Regarding soil quality, the PCA highlighted boron andtotal nitrogen locations in the same quadrant, with the highest contribution to the analysis. Finally, the SQIw showed that soils without maca cultivation presented better quality. Conclusion: This research’s results indicate a need to optimize soil management practices, especially for small farmers, who are the most vulnerable group. In addition, further studies on boron and nitrogen availability in soils cultivated with maca are required, emphasizing areas that exceed 10 years of continuous use.
Spatial Variability of Soil Acidity and Lime Requirements for Potato Cultivation in the Huánuco Highlands
(MDPI, 2024-12-13) Quispe Matos, Kenyi Rolando; Mejía, Sharon; Carbajal Llosa, Carlos Miguel; Alejandro Mendez, Lidiana Rene; Verástegui Martinez, Patricia; Solórzano Acosta, Richard Andi
Soil acidity is a major limiting factor for potato production in Peru's high Andean region. This study aims to predict the spatial variability of soil acidity as a fundamental tool for recommending site-specific liming treatments and to identify the physical-chemical characteristics most closely related to soil acidity. The soil samples were collected from five locations in the province of Pachitea, Huánuco. Descriptive statistics, principal component analysis (PCA), and Pearson correlation analysis were used to identify the soil properties contributing most to total variance and those most strongly correlated with soil acidity. The ordinary geostatistical kriging method evaluated the predictive accuracy for 23 soil properties and liming requirements over a 28,463 ha area, at a spatial resolution of 10 m. Results showed that the Plaza Punta and Buenos Aires locations had more degraded acidic soils, with frequencies between 55% and 100% above the general mean (30.94 ± 24.87%) and the critical threshold (25%) for potato cultivation. Variables such as exchangeable calcium percentage (ECP), Ca2+, Mg2+, sand content, and organic matter strongly correlated with soil acidity, while exchangeable H+ and ECP were the main contributors to the total variance. Geostatistical analysis revealed that Mg2+ and Ca2+ had the highest R² values (0.87 and 0.76, respectively), indicating a strong fit between observed and predicted values in the spatial analysis of soil acidity. It is concluded that the agricultural dolomite requirements in the localities of Plaza Punta and Buenos Aires exhibit high spatial predictability. Additionally, the analysis of diverse soil physicochemical properties is emphasized as critical for determining precise application rates.