Examinando por Autor "Tuesta Trauco, Katerin M."

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Mapping current and future coffee suitability in Peru under climate change: implications for restoration and deforestation-free development
(Frontiers Media S.A, 2026-04-20) Zabaleta Santisteban, Jhon A.; Rojas Briceño, Nilton B.; Silva López, Jhonsy O.; Medina Medina, Angel J.; Tuesta Trauco, Katerin M.; Rivera Fernandez, Abner S.; Silva Melendez, Teodoro B.; Grandez Alberca, Marlen A.; Puscan Rojas, Julio; Salas López, Rolando; Oliva Cruz, Manuel; Cotrina Sanchez, Alexander; Gómez Fernández, Darwin; Barboza, Elgar
Coffee cultivation is central to rural livelihoods and Andean–Amazonian landscapes in Peru; however, it faces increasing pressure from climate change and land-use restrictions. This study aimed to assess the current and future ecological suitability of Coffea arabica at the national scale. A Maximum Entropy (MaxEnt) modeling framework was applied, integrating high-resolution bioclimatic, topographic, and edaphic variables. Model performance was robust (mean AUC = 0.858), and variable importance was evaluated using jackknife tests and contribution metrics. Elevation, precipitation of the driest quarter (bio17), soil nitrogen content, and bulk density were identified as the main determinants of habitat suitability. Under current climatic conditions, highly suitable areas cover 42,322.95 km2 (3.3% of Peru), mainly along the eastern Andean slopes. Spatial exclusion scenarios revealed a pronounced funnel effect in effective land availability, with reductions exceeding 80% when forest-cover constraints were applied. Approximately 39.8% of highly suitable areas overlap with degraded lands, highlighting opportunities for productive restoration through agroforestry systems. Future projections under SSP1–2.6 to SSP5–8.5 scenarios indicate consistent contractions of highly suitable areas (–23% to –42%) and an upslope shift toward higher elevations, while unsuitable areas expand by 4%–5% nationally. These findings provide spatially explicit evidence to support climate-smart territorial planning, restoration prioritization, and sustainable coffee development under accelerating climate change.
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.