Modelamiento espacial y mapeo de la variabilidad foliar de manganeso en Rosa spp. bajo condiciones de invernadero

This study evaluated the spatial variability of manganese (Mn) leaf concentrations in greenhouse grown roses (Rosa spp.) using interpolation techniques applied to spectral data and compared the performance of Inverse Distance Weighting (IDW) and Ordinary Kriging (OK). Four foliar samplings (S1-S4) were tested in 25 plots, with data validated by laboratory analyses and interpolated using both methods. Accuracy was assessed through RMSE (root mean square error), MAE (mean absolute error), bias, R², and Concordance Correlation Coefficient (CCC). Results showed that neither method was consistently superior. In S1, S3, and S4, both methods achieved R² and CCC values above 0.70 with RMSE ranging from 13 to 16, while in S2, IDW clearly outperformed kriging due to a poor variogram fit. Overall, IDW provided greater stability in scenarios with weakly defined structures, whereas kriging more realistically captured heterogeneity when semivariograms revealed clear spatial dependence. Local discrepancies between methods reached ±20 mg kg-1, mainly at block borders and in areas with lower sampling density. These findings demonstrate that the choice of interpolation method depends on the quality, sampling density, and the nature of the analyzed variable. This work represents a novel contribution to greenhouse floriculture by introducing spatial interpolation tools as decision-support systems for precision agriculture, enabling the visualization of intra-block variability and the design of more targeted fertilization strategies with potential integration into digital agricultural management platforms in the future.