Covariates in sample planning optimization for digital soil fertility mapping in agricultural areas

Abstract

The study aimed to evaluate whether the use of covariates in sample optimization helps characterize the spatial variability of soil chemical properties. To this end, data from two agricultural areas located in southeastern Brazil were used. The covariates were used at two stages: sample planning optimization and building predictive methods. The covariates were divided into five categories: relief, vegetation, soil, management, and spatial coordinates. We compared two criteria in sample planning optimization: one aims to ensure spatial distribution by minimizing the mean squared shortest distance (MSSD); the other integrated four criteria through a multipurpose function (SPAN), aiming to represent the distribution and correlation between the covariates, the minimization of the mean squared shortest distance and the distribution of pairs of points for each lag size of the semivariogram. We evaluated three predictive methods (Robust Ordinary Kriging (ROK), Random Forest (RF), and Random Forest-Robust Kriging (RF-RK)) and a null model (mean-based) to predict four soil properties (Cation exchange capacity (CEC), Base saturation (V), Potassium (K) and Phosphorus (P)). We observed that not all areas would benefit from the inclusion of covariates in the sample planning. When our previous knowledge of variability is limited or unknown, we need to evaluate the management in the areas. When the cropping system is more complex and diversified, spatial coverage sampling is more feasible, using the covariates only on the prediction models.