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Optical diffuse reflectance sensing is a potential approach for rapid and reliable on-site estimation of soil properties. One issue with this sensing approach is whether additional calibration is necessary when the sensor is applied under conditions (e.g., soil types or soil moisture conditions) different from those used to generate an initial calibration, and if so, how many sample points are required in this additional calibration. In this study, these issues were addressed using data from ten fields in five states in the U.S. Corn Belt. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on the ratio of standard deviation to RMS error (RPD), a statistic commonly used in spectral analysis. When sample data from the field where calibrations were to be applied (i.e., test field) were included in the calibration stage (full information calibration), RPD values of prediction models were increased by an average of 0.55 (from 1.08 to 1.63) compared with results from models not including data from the test field (calibration without field-specific information). Including some samples from the test field (hybrid calibration) generally increased RPD to 90% of that from full information calibration (average increase = 0.49) by using data from 8 to 20 soil cores, with little further improvement given additional data. Using test field points as a bias adjustment (two-stage calibration) increased RPD by an average of 0.29 with two to six sample points, a finding that was confirmed by Monte Carlo simulation. These results show the importance of including in a calibration set samples similar (i.e., obtained from the same or similar fields) to those in the test set. These similar samples could be included directly in the calibration or could be used to implement a post-calibration bias adjustment. Although results were more accurate with the recalibration approach, the bias adjustment approach was more efficient computationally and required less data. Thus, either might be preferred depending on specific circumstances.