In plant breeding the impacts of genotype by environment interactions and the challenges to quantify these interactions has long been recognized. Both macro and microenvironment variations in precipitation, temperature and soil nutrient availability have been shown to impact breeder selections. Traditionally, breeders mitigate these interactions by evaluating genotype performance across varying environments over multiple years. However, limitations in labor, equipment and seed availably can limit the number of testing locations a breeding program can reasonably maintain throughout a growing season. Although the total area of a test location within a breeding program is relatively small compared to production agriculture, these small plots require added spatial resolution to effectively quantify variability. The development of on-the-go and high throughput soil sensors are a potential solution for plant breeders to quantify spatial variation, as they have the capability to collect a large sample set without significantly adding time or increasing cost through laboratory soil analysis. The objective of this study was to evaluate multiple soil sensor platforms and their ability to capture the soil spatial variability of experiments within the Kansas State Wheat Breeding (KSWB) program. The Veris MSP3, Veris P4000 and lab analyzed soil cores were collected at seven site years across diverse environments throughout Kansas. Data collected from all three methods were analyzed and ordinary kriging was performed to extrapolate soil values for the entire experiment area.  In addition to individually kriged grid points the interpolated kriged data was partitioned into zones based on the K-means algorithm to determine zonal effects on genotype yield. All assessed breeding populations were grown in a modified augmented design type 2 (MAD2) and analyzed by method three to make spatial corrections based on the experimental design. Spatial adjustments by sensor were made through multivariate model where each soil parameter was a fixed effect covariate. Preliminary results show that spatial zones have a significant effect on population yield for many collected soil parameters ranging from 0.15 to 0.90 Mg ha^-1. Furthermore, individual kriged values demonstrated correlation with to grain yield and the spatial adjusted yield values improved coefficient of variation (CV) over the raw yield data. Likewise, the spatial adjustment CV’s were comparable to the statistical adjustments for all sensor platforms at all locations. These results indicate that soil spatial variability exist within the KSWB program, and that on-the-go soil sensors can aid in accounting for spatial correction in plant breeding.