Soil health | EPFL Graph Search

Soil health is a state of a soil meeting its range of ecosystem functions as appropriate to its environment. In more colloquial terms, the health of soil arises from favorable interactions of all soil components (living and non-living) that belong together, as in microbiota, plants and animals. It is possible that a soil can be healthy in terms of eco-system functioning but not necessarily serve crop production or human nutrition directly, hence the scientific debate on terms and measurements. Soil health testing is pursued as an assessment of this status but tends to be confined largely to agronomic objectives, for obvious reasons. Soil health depends on soil biodiversity (with a robust soil biota), and it can be improved via soil management, especially by care to keep protective living covers on the soil and by natural (carbon-containing) soil amendments. Inorganic fertilizers do not necessarily damage soil health if 1) used at appropriate and not excessive rates and 2) if they bring about a general improvement of overall plant growth which contributes more carbon-containing residues to the soil. The term soil health is used to describe the state of a soil in: Sustaining plant and animal productivity (agronomic focus); Enhancing biodiversity (Soil biodiversity) (ecological focus); Maintaining or enhancing water and air quality (environmental/climate focus); Supporting human health and habitation. sequestering carbon Soil Health has partly if not largely replaced the expression "Soil Quality" that was extant in the 1990s. The primary difference between the two expressions is that soil quality was focused on individual traits within a functional group, as in "quality of soil for maize production" or "quality of soil for roadbed preparation" and so on. The addition of the word "health" shifted the perception to be integrative, holistic and systematic. The two expressions still overlap considerably. Soil Health as an expression derives from organic or "biological farming" movements in Europe, however, well before soil quality was first applied as a discipline around 1990.

Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval (R) thermal analysis (PARTY(SOC)v2.0 and PARTY(SOC)v2.0 (EU))

Juan Carlos Quezada Rivera

Partitioning soil organic carbon (SOC) into two kinetically different fractions that are stable or active on a century scale is key for an improved monitoring of soil health and for more accurate models of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measurable using Rock-Eval (R) thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval (R) thermal analysis data for soil samples from long-term experiments for which the size of the centennially stable and active SOC fractions can be estimated and (2) apply this model to the Rock-Eval (R) data for unknown soils to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of a previously published machine-learning model (Cecillon et al., 2018) that is built upon this strategy. The second version of this model, which we propose to name PARTYsoc, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTY(SOC)v2.0(EU)) predicts the proportion of the centennially stable SOC fraction with a root mean square error of 0.15 (relative root mean square error of 0.27) at six independent validation sites. More specifically, our results show that PARTY(SOC)v2.0(EU) reliably partitions SOC kinetic fractions at its northwestern European validation sites on Cambisols and Luvisols, which are the two dominant soil groups in this region. We plan future developments of the PARTY(SOC) global model using additional reference soils developed under diverse pedoclimates and ecosystems to further expand its domain of application while reducing its prediction error.

COPERNICUS GESELLSCHAFT MBH2021

Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval (R) thermal analysis (PARTY(SOC)v2.0 and PARTY(SOC)v2.0 (EU))

Juan Carlos Quezada Rivera

COPERNICUS GESELLSCHAFT MBH2021