DOI: 10.5281/zenodo.17187559
title: Gap-filled subset of the Peatland Mid-Infrared Database (1.0.0)

description: Introduction

This is a gap-filled subset of the Peatland Mid-Infrared Database (1.0.0) (pmird database) stored in the rds format from the R programming language. Measurements for some peat properties were gap-filled using mid-infrared spectra (MIRS) prediction models described in Teickner and Knorr (2025) or calculated from element contents or bulk densities using auxiliary models.




Format

File irp_pmird_gap_filled.rds contains a list with the following elements:



meta: A data frame with a row for each record (id_measurement) in the pmird database for which attributes were gap-filled and three columns: id_measurement, id_sample, id_measurement. Values of these columns identify unique records in the pmird database.


The remaining elements are all data frames with a row for each row in meta and each column representing a peat property.





yhat: A data frame with gap-filled values predicted from the MIRS prediction models. For the meaning of the variables, please see Teickner and Knorr (2025) and the documentation of the prediction models in the R packages irpeatmodels (Teickner 2025a) and irpeat (Teickner 2025b).




yhat_auxilliary: A data frame with gap-filled values computed without MIRS prediction models. Gap-filled values are available for the following peat properties:



C_to_N_3 (C/N), O_to_C_3 (O/C), H_to_C_3 (H/C), nosc_2 (nominal oxidation state of carbon, NOSC): Values are computed from element contents measured with elemental analyzers.

dgf0_3 (standard Gibbs free enrgy of formation): Values are computed from element contents measured with elemental analyzers with auxiliary models as described in Teickner and Knorr (2025).

volume_fraction_solids_1 (volume fraction of solids), non_macroporosity_1 (volume fraction of non-macropores), macroporosity_1 (volume fraction of macropores), saturated_hydraulic_conductivity_1 (saturated hydraulic conductivity), dry_thermal_conductivity_1 (dry thermal conductivity): Values are estimated with pedotransfer functions described in Teickner and Knorr (2025) from bulk density measurements.

specific_heat_capacity_1 (specific heat capacity): Values are estimated with a pedotransfer function described in Teickner and Knorr (2025) from N content measurements.





is_in_training_pd: A data frame with a logical value for each entry indicating whether the MIRS used for gap-filling of values in yhat is within the training prediction domain of the respective MIRS prediction model (TRUE) or not (FALSE). For the definition of training prediction domain, see Teickner and Knorr (2025).




is_in_testing_pd: A data frame with a logical value for each entry indicating whether the MIRS used for gap-filling of values in yhat is within the testing prediction domain of the respective MIRS prediction model (TRUE) or not (FALSE). For the definition of training prediction domain, see Teickner and Knorr (2025).






Usage notes

To load the data within an R session, the following R packages need to be installed: tibble, posterior, and units. The rds file containing the data can be loaded as follows:

d <- readRDS(file = file, refhook = \(x) new.env())

Here, file is the path to the rds file.

The columns in yhat and yhat_auxilliary are rvar objects from the posterior package (https://mc-stan.org/posterior/articles/rvar.html).




Data sources

Data in the database were derived from the following sources: De la Cruz, Osborne, and Barlaz (2016), Hodgkins et al. (2018), Knierzinger et al. (2020), Knierzinger (2020), Münchberger (2019), Münchberger et al. (2019), Schuster et al. (2022), Drollinger, Kuzyakov, and Glatzel (2019), Drollinger et al. (2020), Agethen and Knorr (2018), Kendall (2020), L. I. Harris et al. (2023), L. Harris and Olefeldt (2023), Pelletier et al. (2017), Teickner, Gao, and Knorr (2021), Teickner, Gao, and Knorr (2022), Heffernan (2019), Heffernan et al. (2020), Broder et al. (2012), Anzenhofer (2014, unpublished), Mathijssen et al. (2019), Wagner (2013), Hömberg (2014), Berger et al. (2017), Berger et al. (2018), T. R. Moore et al. (2019), Diaconu et al. (2020), Gałka, Hölzer, et al. (2022), Gałka, Diaconu, et al. (2022), L. I. Harris et al. (2018), L. I. Harris et al. (2019), Boothroyd et al. (2021), Worrall (2021), Reuter et al. (2019b), Reuter et al. (2019a), Reuter et al. (2020), T. Moore et al. (2005), Turunen et al. (2004).




Acknowledgements

Development of this database was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/23-1 to Klaus-Holger Knorr and grant no. PE 1632/18-1 to Edzer Pebesma.




References



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