Examinando por Autor "Contreras Pino, Duglas Lenin"

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Vis-NIR spectroscopy and machine learning for prediction of soil fertility indicators and fertilizer recommendation in Andean highland and rainforest agroecosystems
(MDPI, 2026-04-26) Pizarro Carcausto, Samuel Edwin; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner; Contreras Pino, Duglas Lenin; Ñaupari, Javier; Cano, Deyvis; Patricio Rosales , Solanch Rosy; Loayza, Hildo; Apolo Apolo, Orly Enrique
This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R2 > 0.70), while pH and texture fractions showed moderate performance (R2 = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K2O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients.
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.