Examinando por Materia "Cation exchange capacity"

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Bioestimulantes en la agricultura: Extractos húmicos
(Instituto Nacional de Innovación Agraria (INIA), 2026-01) Amaringo Córdova, Luiz Paulo; Camacho Villalobos, Alina Alexandra
Un bioestimulante vegetal es una sustancia o microorganismo que, aplicado a la planta o a la rizósfera -independientemente de su contenido de nutrientes-, estimula procesos naturales de nutrición, con el fin de mejorar la eficiencia en el uso de nutrientes, la tolerancia al estrés abiótico y también la disponibilidad de nutrientes. A diferencia de los fertilizantes que suministran nutrientes, los bioestimulantes trabajan en mejorar la absorción y eficiencia nutricional, así como en aumentar la resistencia al estrés abiótico. Pueden incluir extractos húmicos, macro y microalgas, hidrolizados de proteínas de origen animal o vegetal, silicio, extractos de plantas, hongos micorrizas arbusculares y rizobacterias promotoras del crecimiento vegetal. Pueden contener hormonas vegetales, extractos de algas marinas, aminoácidos, enzimas, vitaminas como la tiamina.
Critical edaphic and altitudinal factors influencing cation exchange capacity in coffee-growing soils of northeastern Peru: implications for sustainable fertility management
(Frontiers Media SA, 2026-05-05) Díaz Chuquizuta, Henry; Manrique Gonzales, Luis Fernando; Sánchez Ojanasta, Martín; Cuevas Giménez, Juan Pablo; Carbajal Llosa, Carlos Miguel; Cuellar Condori, Néstor Edwin; Martínez Zapata, Boris Guillermo; Vallejos Torres, Geomar
Introduction: Effective cation exchange capacity (ECEC) is a key indicator of soil fertility and sustainable soil management assessment in coffee-growing systems. Methods: This study aimed to identify the principal edaphic and altitudinal factors explaining ECEC variability in 69 soil samples collected from coffee farms in northeastern Peru. Results: ECEC results exhibited substantial variation, ranging from 0.14 to 55.49 cmol(+)·kg⁻¹ (mean = 15.21; SD = 12.47), and were significantly correlated with organic matter (r = 0.71), clay content (r = 0.62), exchangeable acidity (r = -0.63), and altitude (r = 0.33). Principal component analysis accounted for 64.3% of the edaphic variability, identifying Ca²⁺, pH, Mg²⁺, and exchangeable acidity as the most influential variables. The Random Forest model demonstrated high predictive accuracy (R² = 0.93; root mean square error (RMSE) = 2.1 cmol(+)·kg⁻¹), outperforming the generalized additive model (GAM) and identifying Ca²⁺ as the most important predictor (IncMSE% = 3177.37). A functional altitudinal gradient was also evident: areas above 1150 m.a.s.l. showed higher acidity and aluminium content, whereas areas below 900 m.a.s.l. exhibited greater base saturation and higher ECEC. Discussion: These findings support the development of site-specific fertilization strategies and soil–climate zoning, emphasizing the value of integrating multivariate analyses with machine-learning models as key tools for optimizing fertility management and coffee crop productivity in tropical mountain ecosystems; where soil texture represents a key factor influencing coffee sustainability, as greater nutrient retention capacity and improved nutritional balance are associated with enhanced potential for sustainable production and reduced environmental impact.