Soil C and N models that integrate microbial diversity

Abstract

Industrial agriculture is yearly responsible for the loss of 55-100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influencing C and N cycles. Soil microorganisms play a central role in SOM dynamics. For instance, microbial diversity may explain up to 77 % of carbon mineralisation activities. However, soil microbial diversity is actually rarely taken into account in models of C and N dynamics. Here, we review the influence of microbial diversity on C and N dynamics, and the integration of microbial diversity in soil C and N models. We found that a gain of microbial richness and evenness enhances soil C and N dynamics on the average, though the improvement of C and N dynamics depends on the composition of microbial community. We reviewed 50 models integrating soil microbial diversity. More than 90 % of models integrate microbial diversity with discrete compartments representing conceptual functional groups (64 %) or identified taxonomic groups interacting in a food web (28 %). Half of the models have not been tested against an empirical dataset while the other half mainly consider fixed parameters. This is due to the difficulty to link taxonomic and functional diversity.

Fig. 1

Conceptual diagram of the relationship between microbial diversity and soil organic matter (SOM) dynamics. Solid line general relationship; dashed lines area of variability in the relationship; grey points variability of pathways in the relationship; A area in which the relationship is minimum; B area of potential highest variability; C area with no relationship

Fig. 2

Diagram of microbial diversity in current carbon and nitrogen dynamics models. OM organic matter, MB microbial biomass, MIN mineral compounds. Black pool always encountered in models; grey pool specific to certain models

Fig. 3

Diagram of model design (solid arrows with numbers) and stages for integrating microbial community descriptors (dashed arrows with letters). Stages correspond to (1) interactions between analysis of experimental/observed data that enable making hypotheses and hypotheses that influence future experiments, (2) translation of hypotheses into mathematical language, (3) model calibration and validation, (4a) simulations for testing hypotheses enabling (5) experiment/hypothesis interactions or (4b) for predictions, (A) search for best microbial community descriptors, (B) statistical learning, (C) coupling mechanistic modelling with statistical modelling, (D) sensitivity and uncertainty analyses, which help (E) in all model design

Fig. 4

Diagram of a simple model of decomposition of a substrate