Soil microbiome is one of the most important components influencing biogeochemical cycles. Changes in the dominance of different microbial functional groups can result in a community that, due to the changes in microbial enzymes, can respond more or less rapidly to decomposition rates, synthesis of organic matter, nutrient availability and soil structure (Brangarí et al., 2021, Wu et al., 2024). The size and composition of soil microbiome is influenced by variables such as plant species, soil moisture and temperature, pH and nutrients availability (Naylor et al., 2022), which in turn are influenced by climate conditions and agronomic practices. Estimating the soil microbiome composition is therefore crucial to deeper understanding processes such as crop development, carbon (C) and nitrogen (N) uptake, soil nutrient retention, drought tolerance and pest resistance (Lutz et al., 2023).
Despite the large importance of soil microbial composition at determining magnitude and patterns of biogeochemical cycles, the majority of crop and biogeochemical models currently existing are not able to well represent this process. For instance, the microbial biomass simulated by STICS (Brisson et al., 1998) and EPIC (Izaurralde et al., 2006) varies according to N availability in the soil organic matter (SOM) decomposition, without considering microbial species dynamics. Similarly, the pools of models such as RothC (Coleman and Jenkinson, 1996), CENTURY (Parton, 1996), APSIM (Probert et al., 1998), DayCent (Parton et al., 1998), FASSET (Berntsen et al., 2003) Report fixed values of C/N ratios.
This poor representation is mainly related to the lack of detailed algorithms to simulate, for example, SOM turnover driven by soil microbial biomass, the partitioning of different incorporation of decomposable C pools (i.e., lignin and cellulose) from crop residues into soils, the effect of N deficiency on SOM decomposition, and gas transport in soils. These processes should be incorporated into process-based biogeochemical models as driven by soil microbiome to provide more reliable estimates of C and N while reducing uncertainties.
To this end, the RothC submodel implemented within the GRASSVISTOCK model (Leolini et al., 2023) has been improved to take into account seasonal evolution of the soil microbiome and the related effect of agronomic practices. Specifically, new mathematical approaches reproducing the response of microbiota activity to soil temperature and water availability numerically quantify the seasonal trend of the enzymatic activity of the soil microbiota communities (Zhao et al., 2024; Babic et al., 2024; Ghodizadeh et al., 2024) will be integrated within the GRASSVISTOCK model and then validated against a measured available data of the grassland test site in Italy.