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Abstract. The degree of decomposition of peat (γ) is useful to understand peatland degradation and peat accumulation, to reconstruct past net primary production (NPP), and to improve peatland models. None of the available decomposition indicators allows to estimate γ with sufficient accuracy. We suggest prediction of γ measured in litterbag experiments from mid-infrared spectra (MIRS) as a novel decomposition indicator, γMIRS, and compute prediction models for γMIRS with available litterbag experiments and litter data from diverse species from the Peatland Mid-Infrared Database. For individual litter samples, the prediction models fit the data well, have reasonable prediction errors (average RMSE between 0.09 and 0.12 g g−1), and neither confound differences in litter chemistry nor differences in silicate contents with decomposition losses. We show that an underestimation of γ by γMIRS matches theoretical expectations; it can therefore be compensated, using plant macrofossil analysis data as a first approximation to mass fractions of peat components and a simple mixing model, or it can be avoided with component-specific measurements instead of bulk measurements. This allows to estimate γ of peat samples and of dominant litter types and therefore also to reconstruct past NPP. To illustrate the approach, we analyze three cores from European mountain bogs and discuss how it can be used to improve process models and support restoration of peatlands. In particular, we test previously suggested relations between the saturated hydraulic conductivity and γ, illustrate how γ measured on individual litter types may allow to use peat cores as natural litterbag experiments, and define reference states for γ and NPP for the three analyzed peat cores. Improvements to reduce prediction errors of the approach require more diverse litterbag data, especially woody species and more decomposed litter. Further improvements can be achieved with measurements of MIRS on individual macrofossil types instead of bulk measurements, and an improved estimation of mass fractions of macrofossil types in peat samples instead of assuming that macrofossil abundances equal macrofossil masses.