The mulching of agricultural soils has been identified as a viable solution to sequester carbon into the soil, increase soil health, and fight desertification. This is why it is a promising solution for carbon farming in Mediterranean areas. Models are used to project the effects of agricultural practices on soil organic carbon in the future for various soil and climatic conditions, and to help policy makers and farmers assess the best way to implement carbon farming strategies. Here, we modified the widely used RothC model to include mulching practices and their direct and indirect effects on soil organic matter input, soil temperature changes, and soil hydraulic balance. We then calibrated and tested our modified RothC (RothC_MM) using the dataset collected in two field mulching experiments, and we used the tested RothC_MM to estimate the expected soil carbon sequestration due to mulching by the year 2050 for the Valencian Community (Spain). Our results show that RothC_MM improved the fit with the experimental data with respect to basic RothC; RothC_MM was able to model the effects of mulch on soil temperature and soil water content and to predict soil organic carbon (SOC) and CO₂ observations taken in the field.

In this communication, a wide overview of historical Long-Term Experimental Platforms (LTEP) regarding changes in soil organic matter is presented for the purpose of networking, data sharing, experience sharing and the coordinated design of experiments in the area of Earth system science. This serves to introduce a specific platform of experiments regarding biochar application to soil (LTEP-BIOCHAR) and its use for agronomic and environmental purposes (e.g., carbon sequestration, soil erosion, soil biodiversity) in real conditions and over a significative timeframe for pedosphere dynamics. The methodological framework, including the goals, geographical scope and eligibility rules of such a new platform, is discussed. Currently, the LTEP-BIOCHAR is the first of its kind, a community-driven resource dedicated to biochar, and displays around 20 long-term experiments from Europe, the Middle East and Africa. The selected field experiments take place under dynamically, meteorologically and biologically different conditions. The purposes of the platform are (1) listing the field experiments that are currently active, (2) uncovering methodological gaps in the current experiments and allowing specific metadata analysis, (3) suggesting the testing of new hypotheses without unnecessary duplications while establishing a minimum standard of analysis and methods to make experiments comparable, (4) creating a network of expert researchers working on the agronomical and environmental effects of biochar, (5) supporting the design of coordinated experiments and (6) promoting the platform at a wider international level.

After a long debate spanning 20 years, biochar has emerged as a promising land management technique for addressing climate change and improving soil fertility. Biochar is an effective long-term carbon store due to its resistance to decomposition compared to fresh organic matter or compost, and it has the potential to stabilize soil organic matter when added to the soil. However, there is a lack of long-term data and knowledge about Soil Organic Carbon (SOC) stocks due to a lack of historical databases. Studies have shown that few experiments have lasted over 3 and focused on estimating SOC increase from biochar application. Additionally, few of these studies have measured biochar decay rate. In line with the lack of a consistent number of historical databases we developed the LTEP-BIOCHAR (https://site.unibo.it/environmental-management-research-group/en/activities/long-term-platform): a specific platform for experimenting with biochar application to soil with the agronomic and environmental purposes such as carbon sequestration, soil erosion, and soil biodiversity in real conditions and over a significant timeframe. The LTEP-BIOCHAR is community-driven resource dedicated to biochar and includes around 22 long-term experiments from Europe, the Middle East, and Africa. The platform aims to list active field experiments, identify methodological gaps in current experiments, suggest new hypotheses, establish a minimum standard of analysis, create a network of expert researchers, support the design of coordinated experiments, and promote the platform at a wider international level. Next steps in research will include collaboration with experts in the domain of pyrogenic carbon from vegetation fires and finding agreement among practitioners on the mean residence time of C-biochar and related measurements (e.g. isotopic signature, loss of ignition, near-infrared spectroscopy).

Mulching of agricultural soils has been identified as a viable solution to sequester carbon into the soil, increase soil health and fight desertification; as such, it is an interesting option for carbon farming in Mediterranean areas. Models are used to project the effects of agricultural practices on soil organic carbon in the future for various soil and climatic conditions, and to help policy makers and farmers assess the best way to implement carbon farming strategies. Here, we modify the widely used RothC model to include mulching practices and their direct and indirect effects on soil organic matter input, soil temperature changes, and soil hydraulic balance. We then calibrated and validated our modified RothC (RothC_MM) using the dataset collected in a field mulching experiment described in detail in a companying article, and used the validated RothC_MM to estimate the expected soil carbon sequestration by year 2050 due to mulching for the Valencian community (Spain). Our results show that RothC_MM improved the fit with experimental data with respect to basic RothC, and was able to predict SOC and CO₂ observations taken in the field, and to model the effects of mulch on soil temperature and soil water content.

The last decade has seen an increase of innovative soil carbon models that takes explicitly into account the microbial community interaction with the soil organic matter, and various state of protection of the soil organic matter itself. This proliferation is fuelled by (a) the recognition that microbial ecology is the main determinant of soil organic carbon mineralization, and (b) that soil organic matter can be protected from microbial degradation in various ways. Of particular interest is the interaction of the organic matter with the mineral fraction of the soil, which can lead to mineral adsorbtion and the formation of soil aggregates. However, the uncertainties about soil microbial ecology and organic matter – mineral fraction have led to the formulation of various soil microbial models, each one modelling some of the aspects of the complex net of interacting processes, but not other. These models often use different assumptions, model structures, and pool definitions. The lack of comparability among models, and the low comparability of models with measurable data, makes it hard to discriminate among them and to use them to assess the driving processes relevant for soil carbon dynamics depending on climatic, soil and vegetation conditions.A first attempt to compare some of these models has been presented in Sulman et al. (2018); however, the lack of a harmonization framework for the models, and the use of lumped model pools/flows such as soil respiration and bulk soil organic carbon, have led to the conclusion that the uncertainties are too elevated to discriminate among the models.Here, we propose a framework to harmonize five different soil microbial models among them (MEND, CORPSE, MIMIC, DEMENT, RESOM), and harmonize them with measurable soil organic matter fraction widely recognized as related to processes of interaction with the soil mineral fraction (aggregates, mineral associated organic matter, dissolve organic matter, and particulate organic matter). We reformulated the five models based on this framework, and analysed them on the same parameter space to understand in which regions of said space the models gave results that were substantially different.The results show that: (a) the model can be clearly distinguished in most regions of the parameters space, (b) it is possible to calculate an index of robustness of the models. This information can help in design specific experiments to test the models and, this way, get insights about the driving processes in certain conditions (different climates, soils, vegetations); moreover, the robustness index can give indication about their applicability to different conditions, which is of utmost importance if they are to inform Earth System Models.