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.
Portable NIR spectroscopy for soil property prediction and fertility assessment in coffee and cocoa systems
(Elsevier Ltd., 2026-09-01) Grandez Alberca, Marlen A.; Puscan Rojas, Julio; Silva Melendez, Teodoro B.; Juarez Contreras, Lily del Pilar; Medina Medina, Angel J.; Zabaleta Santisteban, Jhon A.; Rivera Fernandez, Abner S.; Tuesta Trauco, Katerin M.; Coronel Alcalde, Mirella N.; Roman Peña, Alcides; Pizarro, Samuel; Barboza, Elgar
The study evaluated the potential of portable Near-Infrared (NIR) spectroscopy to predict soil physicochemical properties in coffee and cocoa production systems in Amazonas, Peru. A total of 126 topsoil samples were initially collected using a stratified sampling design. A total of 125 surface soil samples were analyzed using a stratified sampling design. Soil properties were determined using standard laboratory reference methods, and spectral data were acquired with a NeoSpectra portable spectrometer operating in the 1350–2550 nm (FT-NIR) range. Predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Partial Least Squares Regression (PLSR), and their performance was evaluated using nested five-fold cross-validation (CV). The predictive performance of portable NIR spectroscopy varied according to the soil property, production system, preprocessing technique, and machine learning algorithm. Coffee soils showed higher predictive performance than cocoa soils. The best results were obtained for clay (R²CV = 0.775; RPDCV = 2.276) using RF with first-derivative preprocessing, followed by phosphorus (R²CV = 0.772; RPDCV = 2.037) using PLSR with band-depth preprocessing. Soil pH and silt also showed satisfactory predictive performance in coffee soils. In cocoa soils, potassium exhibited the highest predictive capability (R²CV = 0.454; RPDCV = 1.378) using SVM with second-derivative preprocessing, although the overall model performance was lower than that observed for coffee soils. Portable NIR spectroscopy provided potentially useful estimates for selected soil properties, particularly clay, phosphorus, pH, and silt in coffee soils. However, its predictive performance was strongly dependent on the soil property, preprocessing strategy, machine learning algorithm, and production system.