Examinando por Autor "Gómez Fernández, Darwin"

Mostrando 1 - 11 de 11

An analysis of the rice-cultivation dynamics in the lower Utcubamba river basin using SAR and optical imagery in Google Earth Engine (GEE)
(MDPI, 2024-03-08) Medina Medina, Angel James; Salas López, Rolando; Zabaleta Santisteban, Jhon Antony; Tuesta Trauco, Katerin Meliza; Turpo Cayo, Efrain Yury; Huaman Haro, Nixon; Oliva Cruz, Manuel; Gómez Fernández, Darwin
One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.
Economic profitability of carbon sequestration of fine-aroma cacao agroforestry systems in Amazonas, Peru
(MDPI, 2024-03-08) Goñas Goñas, Malluri; Rojas Briceño, Nilton B.; Gómez Fernández, Darwin; Iliquín Trigoso, Daniel; Atalaya Marin, Nilton; Cajas Bravo, Verónica; Díaz Valderrama, Jorge R.; Maicelo Quintana, Jorge L.; Oliva Cruz, Manuel
Currently, the economic profitability of cocoa is being affected by the increasing incidence of pests, low selling prices, high production costs, and the presence of cadmium in cocoa farms, posing a potential risk of crop abandonment. Therefore, the objective of the present research was to evaluate the economic profitability of carbon sequestration of fine-aroma cacao agroforestry systems in Amazonas, Peru, using the economic indicators of NPV, EIRR, and the benefit–cost ratio. For this purpose, 53 small cocoa producers of the APROCAM cooperative were involved, from which data were obtained on the general characteristics of the production system, production and maintenance costs, indirect costs, and administrative costs; in addition, the costs of implementation and maintenance of an environmental services project were calculated to finally make a cash flow projected over 5 years. As part of the results, the economic analysis was carried out on 104.25 hectares of cocoa belonging to the total number of farmers evaluated, who reported an average yield of 957.32 kg of dry cocoa per he. In addition, it was found that the production cost is PEN 3.91/kg of dry cocoa, and the average selling price is PEN 7.38/kg of dry cocoa. After the economic analysis, it was found that the implementation of an environmental services project is profitable (NPV = PEN 1,454,547.8; EIRR = 44% and B/C = 1.86). These results open up an opportunity for cocoa farmers to diversify and increase their income by contributing to climate change mitigation.
Global perspectives on the biodegradation of LDPE in agricultural systems
(Frontiers Media S.A., 2025-01-06) Mendoza Merino, Jani Elisabet; Tineo Flores, Daniel; Chuquibala Checan, Beimer; Atalaya Marin, Nilton; Taboada Mitma, Victor Hugo; Tafur Culqui, Josué; Tarrillo Julca, Ever; Gómez Fernández, Darwin; Goñas Goñas, Malluri; Reyes Reyes, María Andrea
The increasing use of plastics globally has generated serious environmental and human health problems, particularly in the agricultural sector where low-density polyethylene (LDPE) and other plastics are widely used. Due to its low recycling rate and slow degradation process, LDPE is a major source of pollution. This paper addresses the problem of plastic accumulation in agriculture, focusing on LDPE biodegradation strategies. The studies reviewed include recent data and the methodologies used include state-of-the-art technologies and others that have been used for decades, to monitor and measure the degree of biodegradation that each treatment applied can have, including SEM, GCMS, HPLC, and microscopy. The countries investigating these biodegradation methodologies are identified, and while some countries have been developing them for some years, others have only begun to address this problem in recent years. The use of microorganisms such as bacteria, fungi, algae, and insect larvae that influence its decomposition is highlighted. A workflow is proposed to carry out this type of research. Despite the advances, challenges remain, such as optimizing environmental conditions to accelerate the process and the need for further research that delves into microbial interactions in various environmental contexts.
Integrating remote sensing and in-situ data to determine climate diversity and variability in cocoa systems in the provinces of Jaén and San Ignacio, Cajamarca (NW Perú)
(Elsevier, 2024-12-08) Atalaya Marin, Nilton; Goña Goñas, Malluri; Tineo Flores, Daniel; Chuquibala Checan, Beimer; Arce Inga, Marielita; Tarrillo Julca, Ever; Alvarez Robledo, Yeltsin Abel; Tafur Culqui, Josué; Cabrera Hoyos, Héctor Antonio; Gómez Fernández, Darwin
La falta de información sobre la distribución geográfica de los sistemas de cacao, junto con la diversidad de especies y la influencia de los factores climáticos en los rendimientos, representa desafíos para la gestión agronómica de estas plantaciones y la implementación de políticas agrícolas más efectivas. Este estudio tuvo como objetivo mapear el área de cacao, la diversidad de especies y su respuesta a la variabilidad climática histórica en las provincias de Jaén y San Ignacio, Cajamarca, Perú. Se procesaron datos de PlanetScope y Sentinel-1 en Google Earth Engine utilizando el algoritmo de clasificación Random Forest. Se identificaron 4,338.6 ha de sistemas de monocultivo y agroforestería de cacao, logrando una precisión temática del 85% y un índice kappa de 0.81. Se determinó que Musa sp. predomina en altitudes bajas, mientras que Inga edulis mostró mayor dominio en altitudes más elevadas. La aplicación de datos climáticos y de rendimiento del cacao permitió calcular el índice de anomalía estandarizada, evidenciando el impacto notable de la precipitación en la producción de cacao, especialmente en 2021 y 2022. Este enfoque integrado proporciona una comprensión más profunda de los sistemas agroforestales de cacao, estableciendo una base sólida para la toma de decisiones destinadas a optimizar el rendimiento mediante prácticas agrícolas adaptadas a condiciones climáticas específicas y fomentando la biodiversidad mediante la incorporación de especies nativas.
Landsat images and GIS techniques as key tools for historical analysis of landscape change and fragmentation
(Elsevier, 2024-07-28) Gómez Fernández, Darwin; Salas López, Rolando; Zabaleta Santisteban, Jhon Antony; Medina Medina, Angel J.; Goñas Goñas, Malluri; Silva López, Jhonsy O.; Oliva Cruz, Manuel; Rojas Briceño, Nilton B.
Monitoring and evaluation of landscape fragmentation is important in numerous research areas, such as natural resource protection and management, sustainable development, and climate change. One of the main challenges in image classification is the intricate selection of parameters, as the optimal combination significantly affects the accuracy and reliability of the final results. This research aimed to analyze landscape change and fragmentation in northwestern Peru. We utilized accurate land cover and land use (LULC) maps derived from Landsat imagery using Google Earth Engine (GEE) and ArcGIS software. For this, we identified the best dataset based on its highest overall accuracy, and kappa index; then we performed an analysis of variance (ANOVA) to assess the differences in accuracies among the datasets, finally, we obtained the LULC and fragmentation maps and analyzed them. We generated 31 datasets resulting from the combination of spectral bands, indices of vegetation, water, soil and clusters. Our analysis revealed that dataset 19, incorporating spectral bands along with water and soil indices, emerged as the optimal choice. Regarding the number of trees utilized in classification, we determined that using between 10 and 400 decision trees in Random Forest classification doesn't significantly affect overall accuracy or the Kappa index, but we observed a slight cumulative increase in accuracy metrics when using 100 decision trees. Additionally, between 1989 and 2023, the categories Artificial surfaces, Agricultural areas, and Scrub/ Herbaceous vegetation exhibit a positive rate of change, while the categories Forest and Open spaces with little or no vegetation display a decreasing trend. Consequently, the areas of patches and perforated have expanded in terms of area units, contributing to a reduction in forested areas (Core 3) due to fragmentation. As a result, forested areas smaller than 500 acres (Core 1 and 2) have increased. Finally, our research provides a methodological framework for image classification and assessment of landscape change and fragmentation, crucial information for decision makers in a current agricultural zone of northwestern Peru.
Multicriteria evaluation and remote sensing approach to identifying degraded soil areas in northwest Peru
(Taylor & Francis Group, 2024-12-23) Arce Inga, Marielita; Atalaya Marin, Nilton; Barboza Castillo, Elgar; Tarrillo Julca, Ever; Chuquibala Checan, Beimer; Tineo Flores, Daniel; Fernandez Zarate, Franklin Hitler; Cruz Luis, Juancarlos Alejandro; Goñas Goñas, Malluri; Gómez Fernández, Darwin
Soil is a vital nonrenewable resource characterized by rapid degradation and slow regeneration processes. In this study, soil degradation in Jaén and San Ignacio was assessed via a multicriteria evaluation approach combined with remote sensing (RS) data. Nine factors were analyzed classified three categories: environmental, topographic, and edaphological factors. The results revealed that the slope (59.07%) was the main influencing factor, followed by land use and land cover (LULC) (56.36%). The degradation map revealed that 83.48% of the area exhibited moderate degradation, 14.49% low degradation, and 1.56% high degradation. The districts of Pomahuaca and San José de Lourdes demonstrated the largest areas of moderate degradation, accounting for 13.71% and 22.54%, respectively. Bellavista and Huarango exhibited the largest areas of very high degradation, accounting for 0.27% and 0.08%, respectively. The (AHP) method and RS data were employed to assess soil degradation, highlighting the need for sustainable soil restoration and conservation strategies.
Optimizing landfill site selection using Fuzzy-AHP and GIS for sustainable urban planning
(Salehan Institute of Higher Education, 2024-06-01) Zabaleta Santisteban, Jhon Antony; Salas López, Rolando; Rojas Briceño, Nilton Beltrán; Gómez Fernández, Darwin; Medina Medina, Angel James; Tuesta Trauco, Katerin Meliza; Rivera Fernandez, Abner Shelser; Lévano Crisóstomo, José; Oliva Cruz, Manuel; Silva López, Jhonsy Omar
Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially, twelve sub-criteria were identified, grouped into environmental, socio-economic, and physical categories, and then weighted using paired comparison matrices involving nine experts. The sub-criteria were rasterized and classified into four suitability levels. The weighted overlay of sub-criteria maps generated a territorial suitability model. Within the Alto Utcubamba Commonwealth (Amazonas, Peru), 0.069%, 41.70%, 66.934%, 0.20%, and 12.4% of the territory are suitable, moderately suitable, less suitable, unsuitable, and restricted, respectively, for landfill establishment. Subsequently, 16 highly suitable sites were selected based on the required area (S4 polygons ≥ 0.505 ha) in line with the projected solid waste generation over 20 years. Of the 16 selected areas, only 15 met the shape index. The model showed high accuracy (AUC = 0.784) during validation. Furthermore, this study provides a comprehensive framework for making decisions about waste management in developing countries, enhancing understanding of key factors in selecting landfill sites. It also offers a deeper insight into global and local factors that determine the suitability of landfill sites.
Optimizing landfill site selection using fuzzy-AHP and GIS for sustainable urban planning
(Salehan Institute of Higher Education, 2024-06-01) Zabaleta Santisteban, Jhon Antony; Salas López, Rolando; Rojas Briceño, Nilton B.; Gómez Fernández, Darwin; Medina Medina, Angel J.; Tuesta Trauco, Katerin M.; Rivera Fernandez, Abner S.; Lévano Crisóstomo, José; Oliva Cruz, Manuel; Silva López, Jhonsy O.
Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially, twelve sub-criteria were identified, grouped into environmental, socio-economic, and physical categories, and then weighted using paired comparison matrices involving nine experts. The sub-criteria were rasterized and classified into four suitability levels. The weighted overlay of sub-criteria maps generated a territorial suitability model. Within the Alto Utcubamba Commonwealth (Amazonas, Peru), 0.069%, 41.70%, 66.934%, 0.20%, and 12.4% of the territory are suitable, moderately suitable, less suitable, unsuitable, and restricted, respectively, for landfill establishment. Subsequently, 16 highly suitable sites were selected based on the required area (S4 polygons ≥ 0.505 ha) in line with the projected solid waste generation over 20 years. Of the 16 selected areas, only 15 met the shape index. The model showed high accuracy (AUC = 0.784) during validation. Furthermore, this study provides a comprehensive framework for making decisions about waste management in developing countries, enhancing understanding of key factors in selecting landfill sites. It also offers a deeper insight into global and local factors that determine the suitability of landfill sites.
Site Selection for a Network ofWeather Stations Using AHP and Near Analysis in a GIS Environment in Amazonas, NW Peru
(MDPI, 2021-11-28) Rojas Briceño, Nilton B.; Salas López, Rolando; Silva López, Jhonsy Omar; Oliva Cruz, Manuel; Gómez Fernández, Darwin; Terrones Murga, Renzo E.; Iliquin Trigoso, Daniel; Barrena Gurbillón, Miguel; Barboza Castillo, Elgar
Meteorological observations play a major role in land management; thus, it is vital to properly plan the monitoring network of weather stations (WS). This study, therefore, selected ‘highly suitable’ sites with the objective of replanning the WS network in Amazonas, NW Peru. A set of 11 selection criteria for WS sites were identified and mapped in a Geographic Information System, as well as their importance weights were determined using Analytic Hierarchy Process and experts. A map of the suitability of the territory for WS sites was constructed by weighted superimposition of the criteria maps. On this map, the suitability status of the 20 existing WS sites was then assessed and, if necessary, relocated. New ‘highly suitable’ sites were determined by the Near Analysis method using existing WS (some relocated). The territory suitability map for WS showed that 0.3% (108.55 km2) of Amazonas has ‘highly suitable’ characteristics to establish WS. This ‘highly suitable’ territory corresponds to 26,683 polygons (of ≥30 × 30 m each), from which 100 polygons were selected in 11 possible distributions of new WS networks in Amazonas, with different number and distance of new WS in each distribution. The implementation of this methodology will be a useful support tool for WS network planning.
Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru)
(MDPI, 2022-05-01) Atalaya Marin, Nilton; Barboza Castillo, Elgar; Salas López, Rolando; Vásquez Pérez, Héctor Vladimir; Gómez Fernández, Darwin; Terrones Murga, Renzo E.; Rojas Briceño, Nilton B.; Oliva Cruz, Manuel; Gamarra Torres, Oscar Ándres; Silva López, Jhonsy Omar; Turpo Cayo, Efrain
In Peru, grasslands monitoring is essential to support public policies related to the identification, recovery and management of livestock systems. In this study, therefore, we evaluated the spatial dynamics of grasslands in Pomacochas and Ventilla micro-watersheds (Amazonas, NW Peru). To do this, we used Landsat 5, 7 and 8 images and vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI). The data were processed in Google Earth Engine (GEE) platform for 1990, 2000, 2010 and 2020 through random forest (RF) classification reaching accuracies above 85%. The application of RF in GEE allowed surface mapping of grasslands with pressures higher than 85%. Interestingly, our results reported the increase of grasslands in both Pomacochas (from 2457.03 ha to 3659.37 ha) and Ventilla (from 1932.38 ha to 4056.26 ha) micro-watersheds during 1990–2020. Effectively, this study aims to provide useful information for territorial planning with potential replicability for other cattle-raising regions of the country. It could further be used to improve grassland management and promote semi-extensive livestock farming.
Suitability of the Amazonas region for beekeeping and its future distribution under climate change scenarios
(Elsevier, 2025-02-17) Gómez Fernández, Darwin; García, Ligia; Silva López, Jhonsy O.; Veneros Guevara, Jaris; Arellanos Carrión, Erick; Salas Lopez, Rolando; Goñas Goñas, Malluri; Atalaya Marin, Nilton; Oliva Cruz, Manuel; Rojas Briceño, Nilton B.
Beekeeping plays an important role in global food production and the conservation of wild species. However, determining territorial suitability and future distribution under climate change scenarios is a relatively under-studied area in Peru. This study assessed the beekeeping suitability of the Amazonas region and its variation under climate change scenarios in two projected periods (2041-2060 and 2081-2100), according to Shared Socioeconomic Pathways (SSP). The methodological framework integrated the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), and the Hadley Centre Global Earth Model - Global Coupled configuration 3.1 (HadGEM3-GC31-LL) was used for future climate analysis. The beekeeping suitability of the region was determined based on eleven criteria: four climatic, three topographic, and four environmental. The results indicate that beekeeping suitability is distributed as follows: 3.4% (1417.90 km²) with 'High' suitability, 79.2% (33,318.61 km²) with 'Moderate' suitability, 17.2% (7230.26 km²) with 'Marginal' suitability, and 0.2% (83.64 km²) as 'Not suitable'. Moreover, the average temperature across the region is projected to increase by approximately 3 °C under the SSP2-4.5 scenario and between 6 °C and 8 °C under the SSP5-8.5 scenario during the projected periods. Precipitation will decrease in the northern part of the region, while the southwestern part will experience an increase. In the highly suitable beekeeping area, a temperature increases up to 10.8 °C is expected, with frequent variations around 3 °C to 8 °C, affecting more than 500 km². Additionally, a reduction in precipitation up to 311 mm/year is projected, with predominant variations ranging from -49.5 to 32.8 mm/year over approximately 600 km². Therefore, it is suggested to implement strategies to mitigate these upcoming challenges, particularly if the modeled economic development under the SSPs continues. This study modeled and mapped areas with present conditions suitable for beekeeping and future climate behavior. The modeling aims to guide beekeepers and local authorities in developing sustainable practices and implementing preventive measures to address future climatic challenges.