Spatial Interpolation of Foliar Diseases in Oil Palm Nurseries: a Methodological Approach - Garbanzo-León, Jaime (2025)
The use of simple interpolation techniques for the diagnosis of foliar disease can improve timely agronomic management in crop systems. Currently, there is a lack of calibrated and validated tools to accurately apply interpolation methods in agricultural environments, according to the spatial distribution of foliar diseases.This study aims to identify the most appropriate algorithm for interpolating foliar diseases and determining the optimal sample size for cross-validation. A case study was conducted using the leaf severity percentage in oil palm nurseries. This focused on analyzing the performance of the following interpolators: Triangulation, Inverse Distance Weighted (IDW), Natural Neighbor, Cubic Spline, and Ordinary Kriging. Additionally, various sample sizes for cross-validation were examined, with ranges from 2.5 % to 30 %, using the I Moran and Power analysis as metrics. It was found that, when the sample size reached or exceeded 10 % of the total dataset, spatial autocorrelation decreased, and thus the performance of the interpolation method became more critical for prediction.The study concluded that IDW was the most effective interpolation method (α = 0.05) for predicting the spatial distribution of a foliar disease, outperforming the other evaluated algorithms.