A framework for modelling soil structure dynamics induced by biological activity.

Soil degradation is a worsening global phenomenon driven by socio-economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil-crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil-plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil-crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Isothermal microcalorimetry provides new insight into terrestrial carbon cycling.

Energy is continuously transformed in environmental systems through the metabolic activities of living organisms, but little is known about the relationship between the two. In this study, we tested the hypothesis that microbial energetics are controlled by microbial community composition in terrestrial ecosystems. We determined the functional diversity profiles of the soil biota (i.e., multiple substrate-induced respiration and microbial energetics) in soils from an arable ecosystem with contrasting long-term management regimes (54 years). These two functional profiling methods were then related to the soils' microbial community composition. Using isothermal microcalorimetry, we show that direct measures of energetics provide a functional link between energy flows and the composition of below-ground microbial communities at a high taxonomic level (Mantel R = 0.4602, P = 0.006). In contrast, this link was not apparent when carbon dioxide (CO2) was used as an aggregate measure of microbial metabolism (Mantel R = 0.2291, P = 0.11). Our work advocates that the microbial energetics approach provides complementary information to soil respiration for investigating the involvement of microbial communities in below-ground carbon dynamics. Empirical data of our proposed microbial energetics approach can feed into carbon-climate based ecosystem feedback modeling with the suggested conceptual ecological model as a base.

Long-term fertilization of a boreal Norway spruce forest increases the temperature sensitivity of soil organic carbon mineralization.

Boreal ecosystems store one-third of global soil organic carbon (SOC) and are particularly sensitive to climate warming and higher nutrient inputs. Thus, a better description of how forest managements such as nutrient fertilization impact soil carbon (C) and its temperature sensitivity is needed to better predict feedbacks between C cycling and climate. The temperature sensitivity of in situ soil C respiration was investigated in a boreal forest, which has received long-term nutrient fertilization (22 years), and compared with the temperature sensitivity of C mineralization measured in the laboratory. We found that the fertilization treatment increased both the response of soil in situ CO2 effluxes to a warming treatment and the temperature sensitivity of C mineralization measured in the laboratory (Q10). These results suggested that soil C may be more sensitive to an increase in temperature in long-term fertilized in comparison with nutrient poor boreal ecosystems. Furthermore, the fertilization treatment modified the SOC content and the microbial community composition, but we found no direct relationship between either SOC or microbial changes and the temperature sensitivity of C mineralization. However, the relation between the soil C:N ratio and the fungal/bacterial ratio was changed in the combined warmed and fertilized treatment compared with the other treatments, which suggest that strong interaction mechanisms may occur between nutrient input and warming in boreal soils. Further research is needed to unravel into more details in how far soil organic matter and microbial community composition changes are responsible for the change in the temperature sensitivity of soil C under increasing mineral N inputs. Such research would help to take into account the effect of fertilization managements on soil C storage in C cycling numerical models.

Gas chromatographic metabolic profiling: a sensitive tool for functional microbial ecology.

Microbial metabolomics, which consists of a non-targeted analysis of the metabolites released from ('exometabolome') or existing in ('endometabolome') a cell has mostly been used to study the metabolism of particular microbes. Metabolomes also represent a picture of microbial activity and we suggest that the exometabolome may also contain pertinent information for studying microbial interaction networks. Gas chromatography coupled to mass spectrometry is the most commonly used technique in metabolomics studies. It allows a wide range of metabolites to be detected but requires the derivatisation of compounds prior to detection. This type of non-targeted analysis can introduce biases to the detection and quantification of the different metabolites, particularly at the extraction and derivatisation steps. The aims of this study, therefore, were to quantify the sources of variability and to test the sensitivity of the GC metabolic profiling approach to small environmental changes such as shifts in temperature. The temperature sensitivity of metabolic profiles was compared with that of catabolic profiles obtained using Biolog microplates. Analytical variability was compared with biological variability by incubating bacterial strains isolated from soil with fructose at 20 degrees C and by replicating each step of the protocol (incubation, extraction and derivatisation). For both the endo- and the exometabolome, more than 70% of the total variability was of biological origin and principal components analysis clearly separated the strains along the first ordination axis. The endometabolome distinguished bacterial strains at the species level only, whereas separation was evident at the species and group level with the exometabolome. Temperature had a significant but differential effect on the metabolite production of the bacterial strains whilst their catabolic profiles remained relatively unaffected. The exometabolome was more sensitive to temperature shifts than the endometabolome, suggesting that this pool may be of interest for studies in environmental functional ecology.