Improving soil property maps for precision agriculture in the presence of outliers using covariates

This study aimed to evaluate the use of multiple covariates in robust geostatistical modeling of soil chemical properties characterized by the presence of outliers. Different spatial prediction methods were compared using data from two agricultural areas located in Brazil´s Southeast: one with rotational grazing and one cultivated with sugarcane. Considering the variable-rate fertilizer prescription in the context of precision agriculture, the use of multiple covariates for the prediction of four chemical soil properties (phosphorus (P), potassium (K), cation exchange capacity (CEC) and base saturation (V)) was evaluated. The covariates data set was divided into five categories representing soil, vegetation, relief, management of the area and geographic. Five methods were used: inverse distance weighting (IDW), robust multiple linear regression (RMLR), robust ordinary kriging (ROK), robust universal kriging with spatial co-ordinates in the trend (RUKcoord) and robust universal kriging with environmental and management covariates in the trend (RUKcovars). The model based on the mean was used as a null reference. In general, the use of covariates in robust prediction methods improves the accuracy of spatial prediction of soil properties in the presence of outliers. However this effect was not observed in all situations, depending on the dataset characteristics and the spatial variability of the fields. The management practices are important information for modeling the trend in digital soil mapping for fertilizer prescription purposes. RMLR produces prediction results that are, at least, equivalent to that of robust geoestatistics.