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A Partial Least Squares (PLS) carbonate (CO3) prediction model was developed for soils throughout the contiguous United States using mid-infrared (MIR) spectroscopy. Excellent performance was achieved over an extensive geographic and chemical diversity of soils. A single model for all soil types performed very well with a root mean square error of prediction (RMSEP) of 12.6 g kg-1 and was further improved if Histosols were excluded (RMSEP 11.1 g kg-1). Exclusion of Histosols was particularly beneficial for accurate prediction of CO3 values when the national model was applied to an independent regional dataset. Little advantage was found in further narrowing the taxonomic breadth of the calibration dataset, but higher precision was obtained by running models for a restricted range of CO3. A model calibrated using only on the independent regional dataset, was unable to accurately predict CO3 content for the more chemically diverse national dataset. Ten absorbance peaks enabling CO3 prediction by mid-infrared (MIR) spectroscopy were identified and evaluated for individual and combined predictive power. A single-band model derived from an absorbance peak centered at 1796 cm-yielded the lowest RMSEP of 13.5 g kg-1 for carbonate prediction compared to other single-band models. This predictive power is attributed to the strength and sharpness of the peak, and an apparent minimal overlap with confounding co-occurring spectral features of other soil components. Drawing from the 10 identified bands, multiple combinations of 3 or 4 peaks were able to predict CO3 content as well as the full-spectrum national models. Soil CO3 is an excellent example of a soil parameter that can be predicted with great effectiveness and generality, and MIR models could replace direct laboratory measurement as a lower cost, high quality alternative.