Examinando por Autor "Pizarro Carcausto, Samuel Edwin"

Mostrando 1 - 11 de 11

Composition, diversity, and value of ecological importance in Andean grassland ecosystems according to the altitudinal gradient in the Huacracocha micro-watershed, Peru
(Sciencedomain International, 2023-08-12) Yaranga Cano, Raul Marino; Pizarro Carcausto, Samuel Edwin; Cano, Deyvis; Chanamé, Fernan C.; Orellana, Javier A.
Aims: determine the composition and floristic diversity, the similarity between sites based on the distribution of species in the altitudinal gradient, and determine the value of ecological importance, in Andean grassland ecosystems. Study Design: Original research. Place and Duration of Study: This study took place in the Huacracocha micro-watershed in the Central Highlands of Peru, during the rainy season (January - March 2022). Methodology: The agrostological evaluation points were determined taking into account twelve sites of interest were determined, located from the lowest part of the micro-watershed (4091.8 masl) to the part with the highest vegetation cover (4512.27 masl), the agrostological reading process at each evaluation site was carried out using the radial transect method with the line and intercept point technique. Results: We observed the presence of the presence of 78 vascular species, included in 51 genus and 21 families, was found. The dominance of certain species characterized the type of grassland vegetation, and at least 3 species determined the similarity between sites. The alpha diversity index was low, and the value of ecological importance ranged between 0.0062 and 0.2194. Conclusion: It was concluded that the Andean grassland ecosystems are constituted by a complex community of grasslands based on numerous floristic families, genus, and species, likewise, the dominance of species among the shared sites characterizes the vegetation type, and the diversity index and the IVI determine the complex structural characteristics with great biodiversity.
Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12) Pizarro Carcausto, Samuel Edwin; Pricope , Narcisa G.; Vera Vilchez, Jesús Emilio; Cruz Luis, Juancarlos Alejandro; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
Ecological and carcinogenic risk assessment of potentially toxic elements in rangelands and croplands around Lake Junin (Peru): Integrating remote sensing, machine learning, and land cover segmentation
(Elsevier, 2025-08-27) Pizarro Carcausto, Samuel Edwin; Requena Rojas, Edilson Jimmy; Barboza, Elgar; Peña Elme, Eunice Dorcas; Arias Arredondo, Alberto Gilmer; Ccopi Trucios, Dennis
The Junín Lake basin, a critical high-altitude ecosystem in the central Peruvian Andes, faces severe contamination from potentially toxic elements (PTEs) driven by mining activities, agriculture, and urbanization. This study evaluates the spatial distribution, ecological risk, and human health implications of 14 heavy metals, metalloids, and trace elements in surface soils surrounding the lake. Using 211 soil samples, we integrated remote sensing, land cover classification, and Random Forest machine learning models with spectral, edaphic, topographic, and proximity-based environmental covariates to predict contamination patterns and assess risk. Results reveal extreme contamination, with arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) concentrations exceeding ecological thresholds by over 100-fold in agricultural zones. Ecological risk assessments using contamination degree (mCD), pollution load index (PLI), and risk index (RI) indicated that over 99 % of the study area exhibits very high to ultra-high contamination levels. Human health risk analysis identified unacceptable carcinogenic risks from As, Pb, and Cr across adult and pediatric populations, with arsenic presenting the greatest concern. The integration of geospatial tools and machine learning enabled precise identification of contamination hotspots and vulnerable land cover types, demonstrating the value of AI approaches for monitoring contaminated territories. These findings underscore the urgent need for coordinated environmental management, targeted remediation strategies, and community-based monitoring to protect public health and preserve Andean ecosystem integrity.
Ecological and Human Health Risk Assessment of Heavy Metals in Mining-Affected River Sediments in the Peruvian Central Highlands
(MDPI, 2025-09-16) Custodio, María; Pizarro Carcausto, Samuel Edwin; Huarcaya, Javier; Ortega Quispe, Kevin Abner; Ccopi Trucios, Dennis
Heavy metal contamination in rivers is a serious environmental and public health concern, especially in areas affected by mining. This study evaluated the levels of contamination and the associated ecological and carcinogenic risks in the sediments of the Cunas River, located in the central highlands of Peru. Sediment samples were collected from upstream and downstream sections. Several metals and metalloids were analyzed, including copper (Cu), chromium (Cr), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn), antimony (Sb), arsenic (As), and cadmium (Cd). The ecological risk assessment focused on ten of these elements, while carcinogenic and non-carcinogenic risks were assessed for seven metals selected based on their toxicological importance. The results showed that Cd and Pb concentrations were higher in the downstream section. Cd and As exceeded ecological risk thresholds. Regarding human health, As and Pb surpassed the acceptable limits for both the Hazard Index (HI) and the Potential Carcinogenic Risk (PCR). According to EPA guidelines, these values indicate a potentially significant lifetime cancer risk. The main exposure routes include direct contact with sediments and the consumption of aquatic organisms. Continuous monitoring, phytoremediation actions, and restrictions on the use of contaminated water are strongly recommended to reduce ecological and health risks.
Effects of Glomus iranicum inoculation on growth and nutrient uptake in potatoes associated with broad beans under greenhouse conditions
(MDPI, 2025-07-21) Contreras Pino, Douglas Lenin; Pizarro Carcausto, Samuel Edwin; Verastequi Martínez, Patricia; Solórzano Acosta, Richard Andi; Requena Rojas, Edilson Jimmy
The rising global demand for food, including potatoes, necessitates increased crop production. To achieve higher yields, farmers frequently depend on regular applications of nitrogen and phosphate fertilizers. As people seek more environmentally friendly alternatives, biofertilizers are gaining popularity as a potential replacement for synthetic fertilizers. This study aimed to determine how Glomus iranicum affects the growth of potatoes (Solanum tuberosum L.) and the nutritional value of potato tubers when grown alongside broad beans (Vicia faba L.). An experiment was conducted using potatoes tested at five dosage levels of G. iranicum, ranging from 0 to 4 g, to see its impact on the plants and soil. Inoculation with G. iranicum produced variable results in associated potato and bean crops, with significant effects on some variables. In particular, inoculation with 3 g of G. iranicum produced an increase in plant height (24%), leaf dry weight (90%), and tuber dry weight (57%) of potatoes. Similarly, 4 g of G. iranicum produced an increase in the foliar fresh weight (115%), root length (124%), root fresh weight (159%), and root dry weight (243%) of broad beans compared to no inoculation. These findings suggest that G. iranicum could be a helpful biological tool in Andean crops to improve the productivity of potatoes associated with broad beans. This could potentially reduce the need for chemical fertilizers in these crops.
Environmental, economic and social perceptions of community members on the role of water, soil and natural grasslands as a basis for local development in Acopalca, Peru
(Head Start Network for Education and Research, 2024-06-19) Maldonado Oré, Edith M.; Yaranga Cano, Raul Marino; Pizarro Carcausto, Samuel Edwin; Cano, Deyvis
The concept of ecosystem services has gained popularity among academics, researchers and policymakers to support environmental management and biodiversity conservation, so that many development projects in rural areas have merited investment for restoration and improvement of grassland ecosystems accompanied by training programs for the beneficiaries, With this criterion in mind, the study investigated the perception of puna pastoralists in Acopalca, Peru, regarding the degree of knowledge about the significance of the ecosystem services provided by soil-water-grasslands, with the objective of characterizing the environmental, social and economic dimensions of this local perception, through the application of a survey to the representative of the livestock family affiliated to two producers' associations. It was evidenced that cattle-raising families have a limited understanding of the role of the natural resources they directly access and little clarity on the relationship between natural pastures, family income and access to basic services. The results revealed limitations in environmental perception, evidencing a lack of knowledge about the multifaceted contribution of pastures. Social perception showed neutrality in the relationship between pastures and family income, and a discrepancy in access to basic services. The comparison between associations highlighted significant differences, indicating the need for training strategies adapted to the local idiosyncrasies of the beneficiaries. In conclusion, addressing the deficiencies identified in community understanding was essential to strengthening sustainable natural resource management in Acopalca. It highlights the importance of designing specific training programs, considering the particularities of each group, to promote self-management and community participation and thus achieve more comprehensive and sustainable local development.
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.
Simulation of soil organic carbon potential sequestration for high Andes Peruvian croplands
(Sociedade Brasileira de Ciência do Solo, 2025-10-06) Carbajal Llosa, Carlos Miguel; Vera Vílchez, Jesús Emilio; Pizarro Carcausto, Samuel Edwin; Mestanza, Carlos
Soil organic carbon (SOC) sequestration in croplands represents a significant opportunity to mitigate climate change by removing carbon dioxide from the atmosphere. Simulation tools are increasingly used to assess the impact of climate change and soil management on soil organic carbon stock dynamics. Although Andean soils typically store large amounts of organic carbon, agricultural practices, especially plowing, may deplete these stocks, creating a need to understand these dynamics better. Here, we show the soil organic carbon sequestration potential in croplands in the Peruvian Andean region over 50 years. Soil organic carbon content and bulk density were spatially predicted across the study area using 100 georeferenced soil samples to quantify organic carbon stocks. Spatial interpolation was performed using Ordinary Kriging with exponential and spherical variogram models, which provided the best fit to the data. The RothC model was used to simulate changes in soil organic carbon stocks under two contrasting agricultural management scenarios: one without manure application and another with annual application of one ton of manure per hectare. We found that manure application can substantially increase soil organic carbon sequestration in croplands with increases ranging from 105.22 to 214.94 Mg ha-¹ over 50 years. The potential for increased carbon sequestration through manure application could help compensate for losses in other areas of the watershed, particularly grasslands (74.4 % of the area). This study contributes valuable information for developing sustainable land management strategies in Andean agroecosystems.
Study of ecosystem degradation dynamics in the Peruvian Highlands: Landsat time-series trend analysis (1985–2022) with ARVI for different vegetation cover types
(MDPI, 2023-10-31) Cano, Deyvis; Pizarro Carcausto, Samuel Edwin; Cacciuttolo, Carlos; Peñaloza, Richard; Yaranga Cano, Raul Marino; Gandini, Marcelo Luciano
The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation (p < 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized.
Water storage–discharge relationship with water quality parameters of Carhuacocha and Vichecocha lagoons in the Peruvian puna highlands
(MDPI, 2024-09-04) Pizarro Carcausto, Samuel Edwin; Custodio Villanueva, Maria; Solórzano Acosta, Richard Andi; Contreras Pino, Duglas Lenin; Verástegui Martínez, Patricia
Most Andean lakes and lagoons are used as reservoirs to manage hydropower generation and cropland irrigation, which, in turn, alters river flow patterns through processes of storage and discharge. The Carhuacocha and Vichecocha lagoons, fed by glaciers, are important aquatic ecosystems regulated by dams. These dams increase the flow of the Mantaro River during the dry season, supporting both energy production and irrigation for croplands. Water quality in the Carhuacocha and Vichecocha lagoons was assessed between storage and discharge events by using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and multivariate statistical methods. The quality of both lagoons is excellent during the storage period; however, it decreases when they are discharged during the dry season. The most sensitive parameters are pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD). This paper details the changes in water quality in the Carhuacocha and Vichecocha lagoons during storage and discharge events.
Yield estimation based on agronomic traits in vegetables under different biochar levels
(Elsevier B.V., 2025-09-29) Ccopi Trucios, Dennis; Requena Rojas, Edilson Jimmy; Arias Arredondo, Alberto; Taipe Crispin, Maglorio; Marcelo Matero, Jhonny Demis; Pizarro Carcausto, Samuel Edwin
Biochar, a carbon-rich material produced through oxygen-limited pyrolysis of organic biomass, demonstrates exceptional potential as a soil amendment due to its porous structure and stability. This research investigated the impact of guinea pig manure biochar on three vegetable species cultivated in high Andean conditions: spinach (Spinacia oleracea L.), cabbage (Brassica oleracea var.), and chard (Beta vulgaris var.). The study implemented four biochar application rates (0, 10, 20, and 30 t/ha) and measured comprehensive agronomic parameters including leaf count, leaf length, and fresh/dry biomass of both leaves and roots. Simultaneously, UAV-captured multispectral imagery provided spectral indices that were integrated with agronomic data into machine learning models: linear regression, support vector machines (SVM), and regression trees (CART). Results demonstrated significant vegetative growth enhancement and yield increases across all crops, with the 30 t ha-1 application rate producing optimal outcomes. Predictive modeling exhibited remarkable accuracy: spinach analysis via SVM achieved R² = 0.94 and RMSE = 0.32 g; chard analysis through CART delivered R² = 0.92 and RMSE = 0.35 g; and cabbage assessment using CART yielded R² = 0.91 and RMSE = 0.38 g. This research substantiates biochar’s effectiveness as an organic amendment while establishing a reliable framework for crop yield prediction using machine learning algorithms integrated with spectral data. These findings position biochar as a valuable component in sustainable agricultural systems, particularly for vegetable production in challenging high-altitude environments.