SOM Chemical Composition and Physical Aggregation

In the model, soil organic matter is a state variable represented by the sum of dissolved (DOM) and aggregated organic matter (AOM). Addition of exogenous organic matter (EOM) is also taken into account with external inputs added to DOM and partitioned according to their specific chemical composition.

Solid-state 13C NMR spectroscopy has been used to assess the chemical composition of organic matter in litter decomposition studies, with different classes of organic-C compounds related to specific NMR spectral regions (Spaccini et al. 2000; Almendros et al. 2000; Kogel-Knabner 2002). In the frame of the SOMDY model, seven resonance regions have been considered, as reported by previous reference studies (Lorenz et al. 2000; Kogel-Knabner 2002; Mathers et al. 2007; Pane et al. 2011): 0-45 ppm = alkyl C, 46-60 ppm = methoxyl and N-alkyl C, 61-90 ppm = O-alkyl C, 91-110 ppm = di-O-alkyl C, 111-140 ppm = H- and C-substituted aromatic C, 141-160 ppm = O-substituted aromatic C (phenolic and O-aryl C), 161-190 ppm = carboxyl C. For calibration purposes, within each wide reference region, a restricted sequence of signals was selected by choosing those most correlated with litter decay rate. Then, the following ranges have been used and referred to the different SOMDY model layers (Fig. 11.1) to represent SOM quality: 10-19, 53-57, 70-75, 103-106, 132-136, 149-153, 175-180 ppm.

The mathematical formulation of such representation of organic matter quality in the model is then the following:

The physical aggregation of organic matter is represented in the model by either dissolved organic materials (DOM) or three different dimensional classes of aggregates: Micro (micro-aggregates, particle diameter <0.25 mm), Meso (meso-aggregates, between 0.25 and 1 mm), and Macro (macro-aggregates, >1 mm) (see gray box in Fig. 11.1).

According to the physical structure of SOM (11.1) then becomes:

The following processes are considered in the SOM system dynamics:

- Mineral and organic adsorption

- Physical aggregation

- Mineralization

- Microbial turnover

- Agricultural practices

11.2.3 Mineral and Organic Adsorption

The adsorbing surface on which organic compounds are aggregated is a function of both the adsorption surface area of the soil mineral fraction and the residual exposed surface area of neo-formed organic aggregates.

This is implemented in the model by calculating the available mineral adsorbing surface, ASmineral, as the sum of surface area in each textural class (sand, silt, and clay):

ASmineral = sand • ASsand + silt • ASsilt + clay • ASday (11.3)

where ASsand, ASsiit and ASciay are the texture class-adsorbing parameters.

Then, the rate of DOM adsorption is the product of an adsorption coefficient and the available free mineral surface area.

Additionally, as aggregation proceeds through adsorption of organic molecules on soil particles, the model calculates the new surface adsorption created on the newly formed soil aggregates. Differently from the mineral adsorption surface, such organic adsorption surface cannot be saturated, because aggregation process progressively produces new available binding sites for additional DOM, thereby maintaining a somewhat available adsorption surface. For simplicity, organic matter surface adsorption is modeled considering a spherical geometry for organic C aggregates.

11.2.4 Physical Aggregation

Exogenous organic matter (EOM) is considered in the model as an external input to DOM compartment, originating from either litter decomposition or addition of organic amendments by agricultural practise (e.g., compost). DOM can be mineralized with consequent CO2 release or adsorbed by the mineral and organic soil components. Newly formed Micro-aggregates can then further aggregate forming larger particles (Meso and Macro aggregates). The controlling factors of the aggregation processes are described below. The aggregation process is reversible, i.e., the model also simulates degradation from macro- to meso-, and from meso- to micro-fractions. During degradation, a fraction of organic compounds, previously trapped into the aggregates, is also released as dissolved matter that may flow back to the DOM compartment.

11.2.5 Mineralization

The process of mineralization in the model is represented separately for each chemical class of organic compounds and varies according to the level of physical aggregation (e.g., differences among DOM and Micro, Meso, Macro). The CO2 mineralization flow is a function of different parameters:

• Mineralization rate. Decay rate changes according to chemical composition, i.e., model layers have different mineralization rates. These were defined on the basis of an extensive correlation analysis between solid-state 13C NMR spectral regions and observed decay rates of 64 different decomposing litter samples under controlled optimal conditions. In general terms, the model simulates the decomposition of different classes of chemical compounds by calculating the changes in 13C spectral regions. Figure 11.2 shows the comparison between two solid-state 13C NMR spectra, corresponding to fresh and decomposed Quercus ilex litter, and the output obtained by model simulation of selected spectral regions. The result highlights the model capacity to predict litter decay and relative general chemical changes under optimal decomposition conditions.

• Saturation effect on mineralization. The mineralization process is inversely proportional to the available adsorbing mineral surface area. This reflects the fact that in saturated conditions organic matter is more exposed to microbial attack and easier to mineralize. On the other hand, unsaturated mineral particles strongly adsorb residual organic compounds, which become recalcitrant to desorption in the soil solution and to microbial degradation.

• Temperature effect on mineralization. Several reviews describing the temperature effects on mineralization processes are available in the literature (Lloyd and Taylor 1994; Kirschbaum 1995; Rodrigo et al. 1997; Del Grosso et al. 2005). In the SOMDY model, a simple exponential function (as in Roth-C or in CASA, Potter et al. 1993) is applied, in order to relate increasing temperature to a proportional increase of mineralization rate.

200 180 160 140 120 100 80 60 40 20 0

Fig. 11.2 Comparison between real and simulated chemical composition data. Solid-state 13C NMR spectra of Quercus ilex litter either fresh (black line) or decomposed for 120 days (gray line). Horizontal bars represent level of simulated spectral regions (initial values: black; changes after 120 days: gray)

200 180 160 140 120 100 80 60 40 20 0

Fig. 11.2 Comparison between real and simulated chemical composition data. Solid-state 13C NMR spectra of Quercus ilex litter either fresh (black line) or decomposed for 120 days (gray line). Horizontal bars represent level of simulated spectral regions (initial values: black; changes after 120 days: gray)

• Water effect on mineralization. A simple water balance submodel calculates soil water content. The model uses a NASA-CASA-like equation (Potter et al. 1993), as a function of daily precipitation (P, mm), and potential evapo-transpiration (ETP, mm) calculated according to Thornthwaite (1948). The effect of soil water content on the mineralization process is implemented by a sigmoid curve in the water content interval, where no mineralization occurs at a low level of soil water content. Conversely, the increase of water content is proportionally related to an increase in mineralization rate. A negative effect on mineralization rate comes into play at large water content in soil, since the anoxic conditions are to be taken into account.

• Physical protection. Besides the effects of chemical composition, and temperature and water content, the OM mineralization rate also depends on a parameter defined as "physical protection," indicated by h, that is a function of the soil aggregation dimensions (hmicro < hmeso < hmacro). This is to say that aggregation generally produces a protection effect vis-a-vis of DOM, but also that micro-aggregates of organic C are more susceptible to decomposition then meso- and macro-aggregates, unless micro-aggregates are not integrated in larger soil aggregates.

• Chemical protection. Different chemical compounds are obviously different in their resistance to mineralization and this is reflected by the variable decomposition rates observed in real and simulated regions of NMR spectra (Fig. 11.2). The combined presence of various chemical classes can reduce the mineralization rates of most labile components, since they can become incorporated, and thus protected, into domains composed by more resistant fractions. This phenomenon is represented in the model by a parameter named "chemical protection." This is a weighing score varying in the [—1;1] range attributed to each chemical layer which reflects the relative resistance to mineralization. A score of 1 indicates a highly protective action against microbial mineralization; zero is given to compounds unaffected by chemical protection, —1 means an enhancement of mineralization due to easily decomposable compounds. The total chemical protection is then calculated by summing up the contributions of all chemical layers. By this procedure, the model shows a considerable effect on OM mineralization rates, thereby well integrating the physical aggregation state and the chemical nature of organic C. In fact, aggregates of similar size can show different mineralization rates due to their specific chemical composition that may be different from that of neighboring aggregates. In other words, the presence of highly resistant compounds can act as a protective shell slowing down mineralization of labile compounds, and, vice versa, easily decomposable materials may enhance the decay of recalcitrant fractions of SOM.

11.2.6 Microbial Turnover

The model structure-based chemical differences among layers also provide a conceptual frame for implementing a submodel on microbial turnover. During the mineralization processes, a percent fraction of the organic C is converted into microbial biomass. The model does not explicitly describe the processes of microbial feeding, growth and reproduction, but simply calculates the total microbial biomass according to a "metabolic ratio" of all mineralization flows. Then, micro-bial death is implicitly modeled by re-entering the microbial mass in the system through a partitioning related to a reference microbial composition (Kogel-Knabner 2002). In other words, every time the mineralization occurs, the involved microbes are recycled in the DOM compartments (model layers), in coherence with a chemical description of microbial composition, and, thus, the overall organic matter chemical composition is changed in turn.

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