Liming effects on microbial carbon use efficiency and its potential consequences for soil organic carbon stocks

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication

Liming effects on microbial carbon use efficiency and its potential consequences for soil organic carbon stocks

Julia Schroeder1, Claudia Dǎmǎtîrcǎ2,6, Tobias Bölscher3, Claire Chenu3, Lars Elsgaard4, Christoph C. Tebbe5, Laura Skadell1, Christopher Poeplau1

1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany

2 University of Turin, Department of Agricultural, Forest and Food Sciences, Largo Paolo Braccini 2, 10095 Grugliasco TO, Italy

3 Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 22 place de l'Agronomie, 91120 Palaiseau, France

4 Aarhus University, Department of Agroecology, Blichers Allé 20, 8830 Tjele, Denmark

5 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany

6 current address: Euro-Mediterranean Center on Climate Change (CMCC) Foundation, Division on Climate Change Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Via Igino Garbini 51, 01100 Viterbo, Italy

DOI:   10.1016/j.soilbio.2024.109342

In this study, we set out to test the potential of liming as means to control the microbial carobn use efficiency (CUE). We assessed CUE using the 18O-labelling method for soils from three European long-term liming field trials (i.e. Jyndevad, Versailles, and Dürnast). Additionally, the immediate response of CUE to liming in the lab was tested accounting for lime-derived CO2 emission. The lime-induced pH shift was a strong determinant of CUE. However, the relationship between CUE and soil pH followed a U-shaped (i.e. quadratic) curve, suggesting that CUE may be lowest at near neutral soil pH and therefore to interfere with agronomic interests (i.e. high crop yield). To assess the potential contribution of CUE on the net liming effect on SOC stocks, we calculated OC inputs and SOC stocks. Liming had a positive effect on SOC stocks, regardless of the change in CUE. Our results suggest that CUE added to the net liming effect on SOC stocks. 

Statistical analyses and data visualisation were conducted in R v4.1.2 (2021-11-01) (R Core Team, 2020) using RStudio v2022.12.0 (Posit team, 2022). 

The repository includes the following files:

• liming_sample_data_R.csv - 18O-CUE data and measured pH for DK, DA, VB and DL (n=43)
• site_info_R.csv - C, N, bulk density and pH data shared by co-authors for DK, DA and VB (n=32)
• yield_R.csv - yield data shared by co-authors for DK, DA and VB (n=236)
• CO2sources_R.csv - long-formatted data for CO2 source differentiation in the direct liming experiment (n=66)
• C_input_allocation_factors_R.csv - allocation factors to crop types (Jacobs et al. 2020, https://doi.org/10.1007/s10705-020-10087-5 )

• Schroeder_et_al._liming_effect_on_CUE.Rproj - Rproject (load project to work on provided scripts and data)
• load_data.R - loads required data
• liming_on_soil_pH.R -  statistical analysis liming effect on soil pH, creates output for Table 1 (additional figure effect liming on soil pH)
• liming_on_CUE.R - statistical analysis liming effect on CUE, creates output for Tables 2, S1 and S2
• liming_on_CmicCorg.R - statistical analysis liming effect on Cmic/Corg (laboratory liming excluded), creates output for Table 3
• liming_on_microbial_params.R - statistical analysis liming effect on Cmic, Cgrowth, Crespiration (all treatments), creates output for Tables S1 and S2
• liming_on_abundances.R - statistical analysis liming effect on microbial abundances (fungi, bacteria, archaea), creates output for Tables S1 and S2
• liming_on_K2SO4extrC.R - statistical analysis liming effect on K2SO4 extractable C as proxy for DOC, creates output for Table S3 and Figure S1
• z-tranformation_best_fit.R - tests different models to find best fit of z-transformed data over pH
• calculation_C_stocks.R - test on treatment differences in bulk density, calculation of SOC stocks, creates output for Table S4 and Figure 7
• calculation_C_input.R - calculation of C inputs based on yield_R.csv data and C_input_allocation_factors_R.csv, output Figure S3 and Table S5
• calculation_SOC_formation_efficiency.R - calculation of SOC formation efficiency based on estimated marginal mean difference of C stocks and inputs, script requires calculation_C_stocks.R and calculation_C_inputs.R to be run beforehand
• plot_figures.R - plots Figures 2, 3, 4, 5 ,6, and Figures S2 and S4
• plot_Figure8_radar_chart.R - plots Figure 8

• calculation_maximum_relative_error_respiration_rate_estimates.xlsx - Output data from Visual MINTEQ secnarios plus calculation for error estimation