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In arid and semiarid regions, calibrating bulk soil salinity sensing technologies such as electromagnetic induction (EMI) relies on the assumption of uniformity of all soil factors influencing the reading, except soil salinity, to create a calibration model. When potentially perturbing factors are non-homogeneous or interact in a non-systematic way, conditional mean calibration models based on the least squares method fail to completely describe the entire salinity distribution due to the violation of model assumptions (i.e., homogeneity of perturbing factors). Therefore a new approach is needed. The main objective of this study is to produce a salinity calibration model capable of reasonably predicting salinity directly from the EMI signal readings irrespective of the heterogeneity of perturbing factors. Toward this end we collected ground-truth samples and corresponding EMI measurements in 35 agricultural fields covering 495 ha of the Irrigated Middle Bear (IMB) subbasin of Cache County in Utah. Using quantile regression (QR), which makes no assumption about the distribution of error, we estimated a subset of conditional quantiles of salinity as a function of EMI reading. We found that the mean effects estimated by previous models are misleading because they model behavior around the 0.9th quantile of the distribution, and thus grossly underestimate salinities in the lower quantiles. We developed a new EMI weighting procedure to account for the high heterogeneity that may have caused the upper-tailed distributional behavior. Variability was effectively captured and well modeled at specified quantiles of the salinity distribution using the QR technique. Independent validation of selected multiple QR models indicates that at low salinity ranges corresponding to conditional quantile (tau) <= 0.25, the QR models may be applied to any soil with low range salinity.