Soil Organic Matter (SOM) fractions

This dataset contains the original measured Soil Organic Matter (SOM) fractions of a subset of the LUCAS 2009 topsoil dataset. This dataset includes 352 samples for all land uses and 186 samples only for grassland and fores used to derived maps on Particulate Organic Matter (POM) and Mineral-Associated Organic Matter (MAOM)
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Country: 
Italy
Year: 
2019
Language: 

Complete LUCAS soil organic matter (SOM) fractions data (352 samples)

(29.4.2021)

This folder contains the original measured SOM fractions of cropland, grassland, shrubland  and forest subsamples of the LUCAS survey (2009). The SOM was divided by size into:

  • Particulate organic matter (POM, >53 mm)
  • Mineral-associated organic matter (MAOM, <53 mm)

The corresponding fractions were also analyzed for the Nitrogen content,

Additional LUCAS original soil properties are also included. This dataset is an extension and revision of the previous dataset reporting only forest and grassland land cover (see below).

Soil carbon sequestration is seen as an effective means to draw down atmospheric CO2, but at the same time warming may accelerate the loss of extant soil carbon, so an accurate estimation of soil carbon stocks and their vulnerability to climate change is required. Here we demonstrate how separating soil carbon into particulate and mineral-associated organic matter (POM and MAOM, respectively) aids in the understanding of its vulnerability to climate change and identification of carbon sequestration strategies. By coupling European-wide databases with soil organic matter physical fractionation, we assessed the current geographical distribution of mineral topsoil carbon in POM and MAOM by land cover using a machine-learning approach. Further, using observed climate relationships, we projected the vulnerability of carbon in POM and MAOM to future climate change. Arable and coniferous forest soils contain the largest and most vulnerable carbon stocks when cumulated at the European scale. Although we show a lower carbon loss from mineral topsoils with climate change (2.5 ± 1.2 PgC by 2080) than those in some previous predictions, we urge the implementation of coniferous forest management practices that increase plant inputs to soils to offset POM losses, and the adoption of best management practices to avert the loss of and to build up both POM and MAOM in arable soils.

Metadata:

File name: LCS.csv

Spatial coverage: 25 European Union Member States (excluded Romania, Bulgaria, Croatia)

Input data source: LUCAS point data

Fields:

Coarse, clay, silt, sand = %

pH_in_H2O, pH_in_CaCl

OC = organic carbon (g C kg-1);  N = soil nitrogen (g N kg-1)

CaCO3 = g kg-1; available P and K = mg  kg-1; CEC = cmol(+) kg-1

MAT =mean annual temperature (°C), RAIN = annual precipitation (mm);  Ndep_WD_tx = total N deposition (kg ha-1); EROS = soil erosion (Mg ha-1); WT = water table depth (m)*

s_c_prc = % of silt+clay; OC_pom_g_kg = organic carbon in POM (g/kg); OC_sc_g_kg = organic carbon in MAOM (g/kg); N_pom_g_kg = nitrogen in POM (g/kg); N_sc_g_kg = nitrogen in MAOM; OC_tf = POMC+MAOMC; N_tf= POMN+MAOMN

The database contains a field called ‘POINT_ID’, which can be used to join the data with the general LUCAS soil survey (https://esdac.jrc.ec.europa.eu/projects/lucas). Nevertheless, geographical coordinates of LUCAS points (in WSG84) are provided.

*see table S1 of supplementary for data sources

 

R scripts

All the .R files contain the basic elaborations reported in the paper: “Different climate sensitivity of particulate and mineral-associated organic carbon” in press in Nature Geoscience.

1_RF_MAOMpred.R’ Random Forest regression models to predict C and N in MAOM fraction from measured data (LCS.csv)

‘ 2_RF_cross_valid.R’ cross validation of the RF models

‘ 3_dMAOM_POM_pred.R’ multiregression models to predict change in POM and MAOM in relation to temperature, precipitation, sand X precipitation and land cover.

Due to the number of spatial layers necessary  to upscale the above models, we provide only the final maps (in raster format).

However, all the elaboration steps can be seen in this repository:  https://github.com/elugato/SOC_saturation  

If you need further assistance and information, contact: ec-esdac@ec.europa.eu

 

Raster Layers

Resolution= 1 km, projection =LAEA, format = Geotiff

MAOM_g_kg = C content in MAOM (g C kg-1 soil) in the top 20 cm

POM_g_kg = C content in PAOM (g C kg-1 soil) in the top 20 cm

dMAOM_Mg_ha = cumulative change in C stock of MAOM (Mg C ha-1 in 0-20 cm) at ~2080

dPOM_Mg_ha = cumulative change in C stock of PAOM (Mg C ha-1 in 0-20 cm) at ~2080

 

Additional raster multilayers reporting ensemble estimates (see Supplementary Table 2):

MAOM_ENS_g_kg = C content in MAOM (g C kg-1 soil) in the top 20 cm

MAOM_ENS_ Mg_ha = C stock in MAOM (Mg C ha-1) in the top 20 cm

POM_ENS_g_kg = C content in POM (g C kg-1 soil) in the top 20 cm

POM_ENS_ Mg_ha = C stock in POM (Mg C ha-1) in the top 20 cm

Cite as:
Lugato, E., Lavallee, J.M., Haddix, M.L., Panagos, P., Cotrufo, F. 2021. Different climate sensitivity of particulate and mineral-associated soil organic matter. Nat. Geosci. (2021). https://doi.org/10.1038/s41561-021-00744-x
 


SOM fractions for grassland and forest (186 samples)

18.11.2019

This folder contains the original measured Soil Organic Matter (SOM) fractions of grassland and forest subsamples of the LUCAS survey (2009).

 The SOM was divided by size into:

  • Particulate organic matter (POM, >53 mm)
  • Mineral-associated organic matter (MAOM, <53 mm)

The corresponding fractions were also analyzed for the Nitrogen content.

Metadata

Description: Land management for C sequestration is most often informed by bulk soil C inventories, without considering the form in which C is stored, its capacity, persistency and N demand. Recent frameworks suggest that soil C accrual, its persistence and response to N availability can be better described if SOM is broadly divided into a Particulate Organic Matter (POM) and a Mineral Associated Organic Matter (MAOM) pool. POM, being predominantly of plant origin, contains many structural C-compounds with low N content and persists in soil through inherent biochemical recalcitrance, physical protection in aggregates and/or microbial inhibition. MAOM is largely made of microbial products richer in N, and persists in soil because of chemical bonding to minerals and physical protection in small aggregates.

In this study, we used the Land Use/Land Cover Area Frame Survey (LUCAS) database to determine topsoil C and N storage in European forests and grasslands on 9415 geo-referenced points and separate by size POM (2000-53 μm) and MAOM (<53 μm) in more than 180 subsamples.

File name: SOM_fraction.csv

Spatial coverage: 25 European Union Member States (excluded Romania, Bulgaria, Croatia)

Input data source: LUCAS point data

Fieds:

s_c_prc = % of silt+clay;

OC_pom_g_kg = organic carbon in POM (g/kg);

OC_sc_g_kg = organic carbon in MAOM (g/kg);

N_pom_g_kg = nitrogen in POM (g/kg);

N_sc_g_kg = nitrogen in MAOM

The database contains a field called ‘POINT_ID’, which can be used to join the data with the general LUCAS soil survey (https://esdac.jrc.ec.europa.eu/projects/lucas). Nevertheless, geographical coordinates of LUCAS points (in WSG84) are provided.

R workspace and scripts

ll the .R files contain the basic data and elaborations reported in the paper: “Soil carbon storage informed by particulate and mineral-associated organic matter” in press in Nature Geoscience

Please, refers to the instruction contains in the ‘1_master_script.R’ to run the different scripts that reproduce the statistical procedure and results contained in the paper.

 

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