摘要： The lack of soil data, which are relevant, reliable, a?ordable, immediately available, and su?ciently detailed, is still a signi?cant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within ?elds, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, di?erent calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural ?elds in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The ?rst method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both ?elds. The second univariate model derived the calibration from the reference analytics of all samples from one ?eld. The prediction is validated by LIBS data of the second ?eld. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the ?rst ?eld are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the ?rst ?eld and independently on the second ?eld data. The second univariate method yielded better calibration and prediction results compared to the ?rst method, since matrix e?ects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.