Díaz Chuquizuta, HenryManrique Gonzales, Luis FernandoSánchez Ojanasta, MartínCuevas Giménez, Juan PabloCarbajal Llosa, Carlos MiguelCuellar Condori, Néstor EdwinMartínez Zapata, Boris GuillermoVallejos Torres, Geomar2026-05-222026-05-222026-05-05Díaz-Chuquizuta, H., Manrique Gonzales, L. F., Sánchez Ojanasta, M., Cuevas-Giménez, J. P., Carbajal-Llosa, C., Cuellar-Condori, N., Martínez, B., & Vallejos-Torres, G. (2026). Critical edaphic and altitudinal factors influencing cation exchange capacity in coffee-growing soils of northeastern Peru: Implications for sustainable fertility management. Frontiers in Soil Science, 6, Article 1770292. https://doi.org/10.3389/fsoil.2026.17702922673-8619http://hdl.handle.net/20.500.12955/3133Introduction: 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.application/pdfenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/AltitudeAltitudCation exchange capacityCapacidad de intercambio catiónicoMultivariate analysisAnálisis multivariadoRandom forestBosque aleatorioSoil fertilityFertilidad del sueloSoil zoningZonificación de suelosSustainable coffee productionProducción sostenible de caféTropical soilsSuelos tropicalesinfo:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#4.01.00https://doi.org/10.3389/fsoil.2026.1770292Coffea arabica; Suelo; Soil; Materia orgánica; Organic matter; Calcio; Calcium; Magnesium, Magnesio; pH del suelo; Soil pH; Acilla; Clay; Abono; Fertilizers