Gwenaëlle Lashermes

Modelling of organic matter dynamics during the composting process

Composting urban organic wastes enables the recycling of their organic fraction in agriculture. The objective of this new composting model was to gain a clearer understanding of the dynamics of organic fractions during composting and to predict the final quality of composts. Organic matter was split into different compartments according to its degradability. The nature and size of these compartments were studied using a biochemical fractionation method. The evolution of each compartment and the microbial biomass were simulated, as was the total organic carbon loss corresponding to organic carbon mineralisation into CO(2). Twelve composting experiments from different feedstocks were used to calibrate and validate our model. We obtained a unique set of estimated parameters. Good agreement was achieved between the simulated and experimental results that described the evolution of different organic fractions, with the exception of some compost because of a poor simulation of the cellulosic and soluble pools. The degradation rate of the cellulosic fraction appeared to be highly variable and dependent on the origin of the feedstocks. The initial soluble fraction could contain some degradable and recalcitrant elements that are not easily accessible experimentally.

Increasing soil carbon storage: mechanisms, effects of agricultural practices and proxies. A review

The international 4 per 1000 initiative aims at supporting states and non-governmental stakeholders in their efforts towards a better management of soil carbon (C) stocks. These stocks depend on soil C inputs and outputs. They are the result of fine spatial scale interconnected mechanisms, which stabilise/destabilise organic matter-borne C. Since 2016, the CarboSMS consortium federates French researchers working on these mechanisms and their effects on C stocks in a local and global change setting (land use, agricultural practices, climatic and soil conditions, etc.). This article is a synthesis of this consortium’s first seminar. In the first part, we present recent advances in the understanding of soil C stabilisation mechanisms comprising biotic and abiotic processes, which occur concomitantly and interact. Soil organic C stocks are altered by biotic activities of plants (the main source of C through litter and root systems), microorganisms (fungi and bacteria) and ‘ecosystem engineers’ (earthworms, termites, ants). In the meantime, abiotic processes related to the soil-physical structure, porosity and mineral fraction also modify these stocks. In the second part, we show how agricultural practices affect soil C stocks. By acting on both biotic and abiotic mechanisms, land use and management practices (choice of plant species and density, plant residue exports, amendments, fertilisation, tillage, etc.) drive soil spatiotemporal organic inputs and organic matter sensitivity to mineralisation. Interaction between the different mechanisms and their effects on C stocks are revealed by meta-analyses and long-term field studies. The third part addresses upscaling issues. This is a cause for major concern since soil organic C stabilisation mechanisms are most often studied at fine spatial scales (mm–μm) under controlled conditions, while agricultural practices are implemented at the plot scale. We discuss some proxies and models describing specific mechanisms and their action in different soil and climatic contexts and show how they should be taken into account in large scale models, to improve change predictions in soil C stocks. Finally, this literature review highlights some future research prospects geared towards preserving or even increasing C stocks, our focus being put on the mechanisms, the effects of agricultural practices on them and C stock prediction models.

Typology of exogenous organic matters based on chemical and biochemical composition to predict potential nitrogen mineralization

Our aim was to develop a typology predicting potential N availability of exogenous organic matters (EOMs) in soil based on their chemical characteristics. A database of 273 EOMs was constructed including analytical data of biochemical fractionation, organic C and N, and results of N mineralization during incubation of soil-EOM mixtures in controlled conditions. Multiple factor analysis and hierarchical classification were performed to gather EOMs with similar composition and N mineralization behavior. A typology was then defined using composition criteria to predict potential N mineralization. Six classes of EOM potential N mineralization in soil were defined, from high potential N mineralization to risk of inducing N immobilization in soil after application. These classes were defined on the basis of EOM organic N content and soluble, cellulose-, and lignin-like fractions. A decision tree based on these variables was constructed in order to easily attribute any EOM to 1 of the 6 classes.

Enzymatic Strategies and Carbon Use Efficiency of a Litter-Decomposing Fungus Grown on Maize Leaves, Stems, and Roots.

Soil microorganisms can control the soil cycles of carbon (C), and depending on their C-use efficiency (CUE), these microorganisms either contribute to C stabilization in soil or produce CO2 when decomposing organic matter. However, little is known regarding the enzyme investment of microbial decomposers and the effects on their CUE. Our objective was to elucidate the strategies of litter-decomposing fungi as a function of litter quality. Fungal biosynthesis and respiration were accounted for by quantifying the investment in enzyme synthesis and enzyme efficiency. The basidiomycete Phanerochaete chrysosporium was grown on the leaves, stems, and roots of maize over 126 days in controlled conditions. We periodically measured the fungal biomass, enzyme activity, and chemical composition of the remaining litter and continuously measured the evolved C–CO2. The CUE observed for the maize litter was highest in the leaves (0.63), intermediate in the roots (0.40), and lowest in the stems (0.38). However, the enzyme efficiency and investment in enzyme synthesis did not follow the same pattern. The amount of litter C decomposed per mole of C-acquiring hydrolase activity was 354 μg C in the leaves, 246 μg C in the roots, and 1541 μg C in the stems (enzyme efficiency: stems > leaves > roots). The fungus exhibited the highest investment in C-acquiring enzyme when grown on the roots and produced 40–80% less enzyme activity when grown on the stems and leaves (investment in enzymes: roots > leaves > stems). The CUE was dependent on the initial availability and replenishment of the soluble substrate fraction with the degradation products. The production of these compounds was either limited because of the low enzyme efficiency, which occurred in the roots, or because of the low investments in enzyme synthesis, which occurred in the stems. Fungal biosynthesis relied on the ability of the fungus to invest in enzyme synthesis and the efficient interactions between the enzymes and the substrate. The investment decreased when N was limited, whereas the efficiency of the C-acquiring enzymes was primarily explained by the hemicellulose content and its embedment in recalcitrant lignin linkages. Our results are crucial for modeling microbial allocation strategies.

Modeling the release of organic contaminants during compost decomposition in soil.

Composts, incorporated in soils as amendments, may release organic contaminants during their decomposition. COP-Soil is presented here as a new model to simulate the interaction between organic contaminants and compost, using one module for organic matter and one for organic pollutants, with these modules being linked by several assumptions. Published results of laboratory soil incubations using labeled carbon pollutants from compost were used to test the model for one polycyclic aromatic hydrocarbon (PAH), two surfactants and one herbicide. Several simulation scenarios were tested using (i) the organic pollutant module either alone or coupled to the organic matter module, (ii) various methods to estimate the adsorption coefficients (Kd) of contaminants on organic matter and (iii) different degrading biomasses. The simulations were improved if the organic pollutant module was coupled with the organic matter module. Multiple linear regression model for Kd as a function of organic matter quality yielded the most accurate simulation results. The inclusion of specific biomass in the model made it possible to successfully predict the PAH mineralization.

Interacting Microbe and Litter Quality Controls on Litter Decomposition: A Modeling Analysis

The decomposition of plant litter in soil is a dynamic process during which substrate chemistry and microbial controls interact. We more clearly quantify these controls with a revised version of the Guild-based Decomposition Model (GDM) in which we used a reverse Michaelis-Menten approach to simulate short-term (112 days) decomposition of roots from four genotypes of Zea mays that differed primarily in lignin chemistry. A co-metabolic relationship between the degradation of lignin and holocellulose (cellulose+hemicellulose) fractions of litter showed that the reduction in decay rate with increasing lignin concentration (LCI) was related to the level of arabinan substitutions in arabinoxylan chains (i.e., arabinan to xylan or A∶X ratio) and the extent to which hemicellulose chains are cross-linked with lignin in plant cell walls. This pattern was consistent between genotypes and during progressive decomposition within each genotype. Moreover, decay rates were controlled by these cross-linkages from the start of decomposition. We also discovered it necessary to divide the Van Soest soluble (labile) fraction of litter C into two pools: one that rapidly decomposed and a second that was more persistent. Simulated microbial production was consistent with recent studies suggesting that more rapidly decomposing materials can generate greater amounts of potentially recalcitrant microbial products despite the rapid loss of litter mass. Sensitivity analyses failed to identify any model parameter that consistently explained a large proportion of model variation, suggesting that feedback controls between litter quality and microbial activity in the reverse Michaelis-Menten approach resulted in stable model behavior. Model extrapolations to an independent set of data, derived from the decomposition of 12 different genotypes of maize roots, averaged within <3% of observed respiration rates and total CO2 efflux over 112 days.

Simulation of Organic Matter and Pollutant Evolution during Composting: The COP-Compost Model

Organic pollutants (OPs) are potentially present in composts and the assessment of their content and bioaccessibility in these composts is of paramount importance. In this work, we proposed a model to simulate the behavior of OPs and the dynamic of organic C during composting. This model, named COP-Compost, includes two modules. An existing organic C module is based on the biochemical composition of the initial waste mixture and simulates the organic matter transformation during composting. An additional OP module simulates OP mineralization and the evolution of its bioaccessibility. Coupling hypotheses were proposed to describe the interactions between organic C and OP modules. The organic C module, evaluated using experimental data obtained from 4-L composting pilots, was independently tested. The COP-Compost model was evaluated during composting experiments containing four OPs representative of the major pollutants detected in compost and targeted by current and future regulations. These OPs included a polycyclic aromatic hydrocarbon (fluoranthene), two surfactants (4--nonylphenol and a linear alkylbenzene sulfonate), and an herbicide (glyphosate). Residues of C-labeled OP with different bioaccessibility were characterized by sequential extraction and quantified as soluble, sorbed, and nonextractable fractions. The model was calibrated and coupling the organic C and OP modules improved the simulation of the OP behavior and bioaccessibility during composting.

Fate of 14C-organic pollutant residues in composted sludge after application to soil

Organic micropollutants may be present in biosolids, leading to soil contamination when they are recycled in agriculture. A sludge spiked with (14)C-labelled glyphosate (GLY), sodium linear dodecylbenzene sulphonate (LAS), fluoranthene (FLT) or 4-n-nonylphenol (NP) was composted with green waste and the fate of the (14)C-micropollutant residues remaining after composting was assessed after the compost application to the soil. (14)C-residues were mineralised in the soil and represented after 140d 20-32% of the initial activity for LAS, 16-25% for GLY, 6-9% for FLT and 4-7% for NP. The (14)C-residues at the end of composting that could not be extracted with methanol or ammonia were minimally remobilised or even increased for FLT. After 140d, non-extractable residues represented 38-52% of all of the (14)C-residues remaining in the soil for FLT, 50-67% for GLY, 91-92% for NP and 94-97% for LAS and in most cases, less than 1% of the (14)C-residues were water soluble, suggesting a low direct availability for leaching and microbial or plant assimilation. FLT was identified as the main compound among the methanol-extractable (14)C-residues that may be potentially available. The fate of the (14)C-organic pollutant residues in composts after application to soil could be assessed through a sequential chemical extraction scheme and depended on the chemical nature of the pollutant.

COP-compost: a software to study the degradation of organic pollutants in composts

Composting has been demonstrated to be effective in degrading organic pollutants (OP) whose behaviour depends on the composting conditions, the microbial populations activated and interactions with organic matters. The fate of OP during composting involves complex mechanisms and models can be helpful tools for educational and scientific purposes, as well as for industrialists who want to optimise the composting process for OP elimination. A COP-Compost model, which couples an organic carbon (OC) module and an organic pollutant (OP) module and which simulates the changes of organic matter, organic pollutants and the microbial activities during the composting process, has been proposed and calibrated for a first set of OP in a previous study. The objectives of the present work were (1) to introduce the COP-Compost model from its convenient interface to a potential panel of users, (2) to show the variety of OP that could be simulated, including the possibility of choosing between degradation through co-metabolism or specific metabolism and (3) to show the effect of the initial characteristics of organic matter quality and its microbial biomass on the simulated results of the OP dynamic. In the model, we assumed that the pollutants can be adsorbed on organic matter according to the biochemical quality of the OC and that the microorganisms can degrade the pollutants at the same time as they degrade OC (by co-metabolism). A composting experiment describing two different 14C-labelled organic pollutants, simazine and pyrene, were chosen from the literature because the four OP fractions simulated in the model were measured during the study (the mineralised, soluble, sorbed and non-extractable fractions). Except for the mineralised fraction of simazine, a good agreement was achieved between the simulated and experimental results describing the evolution of the different organic fractions. For simazine, a specific biomass had to be added. To assess the relative importance of organic matter dynamics on the organic pollutants’ behaviour, a sensitivity analysis was conducted. The sensitivity analysis demonstrated that the parameters associated with organic matter dynamics and its initial microbial biomass greatly influenced the evolution of all the OP fractions, although the initial biochemical quality of the OC did not have a significant impact on the OP evolution.