The effect of crop diversification and season on microbial carbon use efficiency across a European pedoclimatic gradient

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication
 
The effect of crop diversification and season on microbial carbon use efficiency across a European pedoclimatic gradient

Julia Schroeder1*, Alexander König2, Christopher Poeplau1, Tobias Bölscher3, Katharina H.E. Meurer4, Monika Toleikienė5, Marjoleine Hanegraaf6, Annelein Meisner6, Josef Hakl7, Katharina M. Keiblinger 2, Abad Chabbi8, Marjetka Suhadolc9, Anton Govednik9, Erich Inselsbacher2, Heike Knicker10, Laura Gismero Rodríguez10, and Anke M. Herrmann4

1 Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany
2 University of Natural Resources and Applied Life Sciences Vienna, Department of Forest and Soil Sciences, Institute of Soil Research, Vienna, Austria
3 Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France
4 Department of Soil & Environment, Swedish University of Agricultural Sciences - SLU, Uppsala, Sweden
5 Lithuanian Research Centre for Agriculture and Forestry, Akademija, Lithuania
6 Wageningen University & Research, Wageningen Plant Research, Wageningen, Netherlands
7 Czech University of Life Sciences Prague, Czech Republic
8 INRAE Centre de Recherche Nouvelle-Aquitaine-Poitiers, Unité de Recherche Pluridisciplinaire Prairies & Plantes Fourragères, Lusignan, France
9 University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia
10 Instituto de la Grasa (IG-CSIC), Sevilla, Spain
* Corresponding author: julia.schroeder@thuenen.de

DOI: 

The study aimed to investigate the effect of crop diversification measures (cover crops, ley farming, vegetation stripes) on microbial carbon use efficiency (CUE) and its potential link to SOC accrual in agricultural soils across Europe. The central hypothesis was that the crop diversification treatment results in more efficient microbial use of C, thus enhancing the potential of soils to store C. The effect of treatment was expected to vary between seasons.
Topsoil was sampled from eight experimental crop diversification sites across a pan-European pedoclimatic gradient (Sweden, Netherlands, Lithuania, Czech Republic, Austria, France, Slovenia, and Spain). At five sites, a second sampling was conducted to test the effect of season on CUE (Sweden, Netherlands, France, Slovenia, and Spain). CUE was assessed by the 18O-labelling method. To account for the different experimental layout between sites, a meta-analysis approach was used for statistical analysis. To test for a general pattern of the seasonal variation in CUE across the pedoclimatic gradient, weather data (representing 3-months weather prior sampling) was used to extract seasonal predictors. 
For further details, please see the peer-reviewed publication. 

The R code was developed under R v4.4.0.

The repository includes the following files:

data:

• EJP-EL_COORD_sites.csv - Provides coordinates, reference system, land use, diversification treatment and year of establishment for the experimental sites (n=8). 
• EJP-EL_DATA_sites.csv - Provides texture, TOC and N content, and bulk density data for the experimental sites as provided by site managers (if applicable on plot basis)(n=106).
• EJP-EL_CUE_for_R.csv - Provides assessed per sample observations (n=220)
  WHC: water holding capacity, pH: soil pH measured in 1:5 w/w soil-water solution, TC_perc: total C (%), TIC_perc: total inorganic C (%), TOC_perc: total organic C (%), TN_perc: total N (%), total_DNA_gsoil: total amount of DNA extracted (ug DNA g-1 soil), Cmic_ugC_gsoil: microbial biomass C by CFE-method (ug microbial biomass C g-1 soil), fDNA: unitless conversion factor, Cgrowth_ngC_g_h: microbial growth rate (ng C g-1 soil h-1),  Crespiration_ngC_g_h: microbial respiration rate (ng C g-1 soil h-1), CUE: unitless carbon use efficiency, mass_specific_growth_1_perd: mass specific growth rate (1 d-1), turnover_time_d: turnover rate (d), DW/WW_during_incub: ratio of dry weight to wet weight (indicator of water content) during the time of incubation (g g-1); WC_perc_related_to_DW_at_sampling: water content during the time of soil sampling expressed as percentage water refered to dry weight of soil (%)
• weather_data_b4_sampling_FS_nasa_power_mod_incl_radiation.csv - Extracted daily weather data for the respective site coordinates from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program, i.e. NASA POWER project, for a 3-months period prior to each sampling event: mean daily air temperature at 2 m (T2M), the bias corrected average of total precipitation at the Earth’s surface (PRECTOTCORR), and the total photosynthetically active radiation incident at the Earth’s surface (ALLSKY_SFC_PAR_TOT). 
• Table_S1.xlsx - Provides meta-data of sites including sampling (treatments, depth, number of samples, time), water content at sampling, management (last main crop, cover at sampling, phenological state at sampling, fertilisation dates, type of fertiliser, fertilisation rate, ploughing date, ploughing depth).

Rproj:

• R_project_Schroeder_crop_div_and_season.Rproj - Rproject (load project to work on provided scripts and data)

R scripts:

• 01_EJP-EL_CUE_load_data.R - Loads data from different provided csv and merges them into one data.frame, specifying assignment of categories (e.g. control vs one diversified treatment) and selections for individual analyses (e.g. seasonality (all treatments, 5 sites), EU.gradient (control vs. one diversified treatment, 8 sites)).
• 02_extract_climate_data_sites.R - Extracts climate data from BIOCLIM database (MAT, MAP, Growing season lenght, Köppen Geiger) for site coordinates.
• 03_meta-analysis_crop_diversification_EU_gradient.R - Conducts meta-analysis on EU.gradient selection (control vs diversified treatment, 8 sites) with crop diversification measures as subgroups. Plots results of the meta-analysis. Exports overall test statistics.
• 04_ANOVA_seasonality_effect.R - Plots CUE per site and sampling on seasonality selection. Conducts site-wise ANOVA to test for effects of sampling and treatment on CUE, and whether the effect of treatment varies with sampling (i.e. interaction). 
• 05_define_season_by_weather_data_before_sampling.R - Extracts weather data from indicated NASA database for the 3-months period prior to sampling. Calculates seasonality predictors (see M&M of publication to this study for detailed background). Creates table with calculated predictors.
• 06_Driver_Analysis_seasonality.R - Checks for autocorrelation of seasonal predictors. Conducts linear mixed-effects model for driver analysis. Creates figure presenting results of the driver analysis.
• 07_PCA_pedo-climatic_gradient_EU.R - Conducts and plots a principal component analysis to visualise the spread of pedo-climatic properties across sites.
• 08_LMEM_France.R - Conducts a separate linear mixed-effects model statistics to test wehther ley farming is more similar to cropland or grassland (microbial CUE, Cmic, Cgrowth and SOC) at the Lusignan (France) site.