Mid-infrared reflectance spectroscopy (MIRS) is time- and cost-effective. It was used for quantifying soil inorganic carbon (SIC) concentration in France based on a national library, and performances were evaluated on an independent regional set. Our objective was to improve the accuracy of MIRS predictions based on common multivariate regression, through spiking (enrichment of the national library with some representative target samples) with possible extra-weighting (replication of spiking samples) and local calibration (only using calibration samples that are spectral neighbours of each target samples), which have not been fully explored yet, in combination especially.Global (i.e. common) calibration yielded accurate prediction (standard error of prediction, SEP, was ≈ 5 g kg−1), which could be improved when the library was completed with spiking samples (optimally 10 samples extra-weighted 40 times; SEP = 3.3 g kg−1). Using spiking samples only (without the library) yielded slightly less accurate results (SEP = 3.6 g kg−1). Prediction was more accurate using local calibration without spiking, but on a validation set that was reduced because some validation samples lacked calibration neighbours (SEP = 2.5–2.7 g kg−1). Local calibration with spiking (optimally 10 samples without extra-weight) yielded somewhat less accurate prediction but for the full validation set when few calibration neighbours were required (SEP = 2.7 g kg−1), or higher accuracy on the reduced validation set when many neighbours were required (SEP = 2.3 g kg−1).These accurate predictions demonstrated the usefulness of representative spiking and local calibration for rendering large soil spectral libraries fully operational, while extra-weighting had no additional benefit. Along with more exhaustive spectral libraries, this paves the way for extensive use of MIRS for SIC determination.