ABSTRACT Obtaining knowledge about the distribution of spatial variability of soil properties is crucial to the proper site-specific management. One way to improve the quality of soil mapping is by using auxiliary information (covariate). The objective of this study was to test whether remote and proximal sensing data can assist in soil properties mapping through geostatistical prediction. We worked in an experimental area cultivated with sugarcane located in Sao Paulo State, Brazil, and selected five soil properties: organic matter, CEC, base saturation, K and P availability. Two covariates often used to express soil variation were chosen, one obtained by remote sensing (SWIR2 band) and the other by proximal sensing (apparent soil electrical conductivity – ECa). These covariates were individually and together used in geostatistical interpolation method (kriging with external drift). We found that ECa is a more promising covariate than SWIR2 band from orbital imaging. Such proximal sensing can identify the soil short-range spatial variability. However, when the soil property variability is well explained by the sampling procedure, multivariate geostatistical methods may not improve the mapping accuracy.

ABSTRACT Data interpolation is widely required in precision agriculture. However, its effectiveness is a function of the characteristics of the dataset, being necessary for the evaluation of several parameters. This study aimed to identify how the spatial interpolators, Kriging, and Inverse Distance Weighting, are influenced by the degree of spatial dependence of the variables analyzed and the number of neighbors considered in the interpolation process (sampling neighborhood). Soil samples were collected from three sugarcane fields. By the optimization process, we verified that the sampling neighborhood influences the accuracy of interpolations, but there is not a standard recommendation to follow. Thus, the best sampling neighborhood must ever be optimized for each case when preparing fertilizer prescription maps. Evaluating the performance of interpolations is always important to infer the reliability of the prescription maps, since no index that measures the degree of spatial dependence is effective. Because high prediction errors can occur when spatial dependence is poorly modeled, one cannot expect crop response with the continuous application of fertilizers in variable rates. Thus, work with homogeneous soil zones can be an interesting palliative approach. This study guides precision agriculture practitioners on some points that should be carefully considered in the data interpolation process for generating fertilizer prescription maps.