Measurements in soils have been traditionally used to demonstrate that soil architecture is one of the key drivers of soil processes. Major advances in the use of X-ray Computed Tomography (CT) ...afford significant insight into the pore geometry of soils, but until recently no experimental techniques were available to reproduce this complexity in microcosms. This article describes a 3D additive manufacturing technology that can print physical structures with pore geometries reflecting those of soils. The process enables printing of replicated structures, and the printing materials are suitable to study fungal growth. This technology is argued to open up a wealth of opportunities for soil biological studies.
► X-ray CT provides valuable insight into pore geometry of soil microcosms. ► Novel printing technology produces replicable 3D microcosms reflecting soil structure. ► Printed 3D complex geometries can be used to study biophysical interactions.
•New experimental data set of 3D images of air–water interfaces in a soil.•Remarkably accurate prediction of air–water interfaces by the two-phase LBM.•Acceptable prediction of air–water interfaces ...by the morphological MOSAIC model.
Recent progress in the understanding of soil microbial processes at micrometric scales has created a need for models that accurately predict the microscale distribution of water, and the location of air–water interfaces in pores. Various models have been developed and used for these purposes, but how well they fare against real data has yet largely to be determined. In this context, for the first time, this article compares the prediction of two of these models to experimental data obtained on soil material. The distribution of water and air in soil samples constituted of repacked aggregates, equilibrated at three matric potentials (−0.5 kPa, −1 kPa and −2 kPa), was measured via synchrotron X-ray computed tomography at a resolution of 4.6 µm. Water distribution was simulated by a two-phase lattice Boltzmann model (LBM) and a morphological model (MOSAIC). Results indicate that, when one lifts the assumption, motivated by capillary theory, that a pore can drain only if a connecting pore is already full of air, MOSAIC gives an acceptable approximation of the observed air–water interfaces. However, discretization of pores as geometrical primitives causes interfaces predicted by MOSAIC to have nonphysical shapes. By contrast, LBM is able to predict remarkably well the location of air–water interfaces. Nevertheless, given the huge difference in computing time (minutes versus tens of hours) required to run these two models, it is recommended that further research be carried out on the development of both, in parallel.
•Variation of Ksat values of up to a factor of 100 is found depending on the CT image treatments.•Segmentation methods provide the largest variation in Ksat compared to other CT image treatments.•The ...Kozeny-Carman equation succeeds to explain the Ksat variation.•Kozeny’s constant is positively related to the degree of complexity of pore geometry.
Over the last decade, computed tomography (CT) images have been used increasingly to gain a more complete understanding of the microscale parameters that control the soil processes. Images that are used for that purpose typically are subjected to a number of successive treatments. This article evaluates for a sandy-loam soil, the impact of two CT scan resolutions (26 and 54 μm), three noise reduction levels, four settings of conversion from a 32-bit to an 8-bit image and four segmentation methods (Hapca et al., 2013; Houston et al., 2013a; Schlüter et al., 2010; Otsu, 1979) in an incomplete factorial design, on the morphological metrics and on the numerical calculation of the saturated hydraulic conductivity, Ksat. The calculations reveal variations of up to two orders of magnitude of Ksat. Houston’s and Schlüter’s segmentations appear the most sensitive to the identification of gray voxels in the key throats controlling the water flow. When combined with high noise reduction levels they produce in some extreme cases disconnections of the percolating pores. Hapca’s segmentation produces more robust results. The Kozeny-Carman relation successfully predicted the saturated hydraulic conductivity when using the critical path diameter as the characteristic length, instead of the macroscopic hydraulic radius which appears too integrative to identify the extent of the variation of key throats.
The pore size distribution (PSD) of the void space is widely used to predict a range of processes in soils. Recent advances in X-ray computed tomography (CT) now afford novel ways to obtain exact ...data on pore geometry, which has stimulated the development of algorithms to estimate the pore size distribution from 3D data sets. To date there is however no clear consensus on how PSDs should be estimated, and in what form PSDs are best presented. In this article, we first review the theoretical principles shared by the various methods for PSD estimation. Then we select methods that are widely adopted in soil science and geoscience, and we use a robust statistical method to compare their application to synthetic image samples, for which analytical solutions of PSDs are available, and X-ray CT images of soil samples selected from different treatments to obtain wide ranging PSDs. Results indicate that, when applied to the synthetic images, all methods presenting PSDs as pore volume per class size (i.e., Avizo, CTAnalyser, BoneJ, Quantim4, and DTM), perform well. Among them, the methods based on Maximum Inscribed Balls (Bone J, CTAnalyser, Quantim4) also produce similar PSDs for the soil samples, whereas the Delaunay Triangulation Method (DTM) produces larger estimates of the pore volume occupied by small pores, and Avizo yields larger estimates of the pore volume occupied by large pores. By contrast, the methods that calculate PSDs as object population fraction per volume class (Avizo, 3DMA, DFS-FIJI) perform inconsistently on the synthetic images and do not appear well suited to handle the more complex geometries of soils. It is anticipated that the extensive evaluation of method performance carried out in this study, together with the recommendations reached, will be useful to the porous media community to make more informed choices relative to suitable PSD estimation methods, and will help improve current practice, which is often ad hoc and heuristic.
•The need to evaluate existing methods for pore size distribution estimation is explained.•A review of the theoretical principles of pore size distribution estimation is presented.•A selection of commercially and freely available software for pore size distribution estimation are tested.•Performance of methods on synthetic 3D images and X-ray CT images of soils is analysed.•Recommendations on the use of methods for pore size distribution estimation are made.
► We parameterise a model of soil fungal dynamics to understand soil–microbial interactions. ► A sensitivity analysis assesses the impact of parameters unavailable from literature. ► Biomass ...recycling parameters are the main source of uncertainty in predicting soil fungal growth. ► Further research to reduce the uncertainty associated with these parameters is required. ► This parameterisation is critical to elucidate the role of fungi in C-dynamics in soil.
The role of fungi in soil ecosystem sustainability is poorly understood, as is the extent to which it is affected by the microscale heterogeneity of soils with respect to structure, chemistry and biology. This is due to the complexity of soil ecosystems, presenting significant challenges to their study in situ. Many theoretical and simulation models have been developed to link nutrient levels to colony dynamics. Unfortunately, there is currently no model that can take both structural and nutritional microscale heterogeneity into account, and is parameterised for the soil environment. In this context, the objective of this article is to develop such a 3D spatially explicit model of fungal dynamics, and to calibrate it for a soil system using data from the literature. A sensitivity analysis is carried out to better understand the uncertainties in the input parameters and their effect on colony dynamics in terms of biomass yield and respiration rates. The results highlight simulation outcomes that are most suited to validation by experimentation. The results also indicate that predictions in biomass yield are sensitive to uncertainties in model parameters relative to the soil–fungal complex that at this point are insufficiently understood experimentally and still have to be estimated by model fitting. The latter parameters, which influence biomass yield and respiration, are associated with biomass recycling processes such as adsorption (,αni) desorption (βni, βi), insulation (ζni) and biomass yield efficiency (ɛ1), and translocation (Dv). The model now opens up great opportunities for hypothesis-driven research, combining theoretical models and novel types of experimentation, especially given the recently acquired ability to generate artificial, replicable soil-like microcosms on which to test model predictions.
Image segmentation is a crucial step in understanding the structure of porous materials, subsequent analyses being profoundly dependent upon segmentation accuracy. Computed tomography images of ...naturally occurring heterogeneous materials such as soils are particularly challenging to segment reliably, due to the prevalence of partial volume effects, noise, and other artefacts induced during the image acquisition process. As a result, boundaries between classes of objects, typically pore versus solid in the case of soil, are difficult to identify. Indicator kriging can address these problems in a robust fashion but the computational cost can be very great under some circumstances; this is particularly true under conditions that occur regularly in images of soil. The kriging window size parameter is decisive in obtaining a good quality result at reasonable cost, but is difficult to estimate for an image exhibiting significant heterogeneity. This work demonstrates that, allowing the kriging window size to adapt locally throughout the image provides a very efficient solution. Moreover, it is shown that this can be achieved using a conceptually simple mechanism that involves negligible extra processing cost. The adaptive-window indicator kriging method described in this study achieves easily an order of magnitude improvement in computational performance over a fixed size window implementation without sacrificing quality. In addition, it is shown that, by improving the locality of estimation, the new method is robust when applied to soil images.
► The need to improve segmentation methods for porous media is explained. ► An inherent weakness of conventional “fixed window” indicator kriging is exposed. ► A novel mechanism that locally adapts the size of kriging window is presented. ► Method evaluation uses X-ray CT images of soil; Minkowski functionals are analysed. ► The advantage of an adaptive window is demonstrated and improvements suggested.
•Output variability is due predominantly to soil architecture factors when accessibility is low.•Output variability of uncertain biological factors is high when accessibility is high.•DOC and CO2 are ...good proxies to identify the role of soil architecture on biodegradation.•Model structure strongly impacts the identification of sensitive parameters.
Soil respiration causes the second largest C flux between ecosystems and the atmosphere. Emerging soil carbon dynamics models consider the complex interplay of microscale interactions between the physical and biological drivers of soil organic matter decomposition occurring in the 3D soil architecture. They are expected to provide a way to upscale results to the macroscopic level and as such appear as an alternative modelling approach to the traditional “black-box” macroscopic models. However, these models still need to be tested under a broader range of their parameters values and structures than has been the case to date. We thus conducted uncertainty and global sensitivity analyses to test the robustness of previous predictions on dissolved organic carbon biodegradation obtained by one of these microscopic carbon dynamics models, LBioS. Six parameters of the carbon dynamics module of LBioS, associated with bacterial metabolism and three microscopic 3D descriptors of soil architecture were considered as uncertain inputs. We built two complete factorial designs in which the minimum and maximum of uncertainty intervals are considered. Each factorial design is assigned to a particular structure of the model, one including dormancy of bacteria and the other considering optimal bacterial activity. The scenarios took place in 3D computed tomography images of an undisturbed cultivated soil. The sensitivity indices at different simulations dates were computed with an ANOVA procedure taking into account main effects and interactions among factors. The uncertainty analysis shows that only in the limiting case of low accessibility of resources to bacteria the different microbial metabolisms tested can modify to a small extent the system responses, and uncertainty linked to parameters describing soil architecture becomes preponderant. In the case of optimal accessibility output variability is due predominantly to uncertainty of the microbial metabolism parameters. The sensitivity analysis suggests that whatever the structure of the model, the role of soil architecture in the microbial activity can be evidenced using either DOC or CO2 as proxy measures. Beyond these results, we stress that results of uncertainty and sensitivity analyses of soil carbon models need to be interpreted with caution, dependent as they are on the status of the model itself, as well as on the particular scenarios used in the uncertainty and sensitivity analyses.
Over the last decade, X-ray computed tomography (CT) has been used increasingly to characterise the microscale architecture of soils. As a result significant progress has been made in the acquisition ...and interpretation of X-ray CT data, as well as in the thresholding of 3D greyscale CT images in order to produce binary (black and white) ones. Nevertheless, sizeable uncertainties persist, in particular concerning optimal instrumental settings used to generate the greyscale images. In this context, the key aim of this study is to investigate in detail the effect of scanning resolution and reconstruction settings such as noise reduction and 32-bit to 8-bit mapping interval on the 3D X-ray CT imaging of soil structure and the impact on the performance of thresholding methods. To assess the quality of the X-ray CT greyscale images, measures of contrast, noise and sharpness are proposed and tested on a series of images of five different soil samples. At the same time, performance of four segmentation algorithms, i.e., three methods recently developed to deal specifically with soil samples and Otsu's method as a benchmark, was evaluated using functional measures of 3D binary images, including Minkowski functionals and surface pore connected fraction. Results of these analyses suggest that the acquisition and reconstruction parameters investigated significantly affect the quality of soil images, and the subsequent thresholding process. In particular, it was found that thresholding by any of the four methods is greatly affected by the quality of image sharpness, which for soil images appears to be mainly controlled by the scanning resolution. As a result, it is concluded that no matter what reconstruction resolution is required in a study, in order to allow an accurate identification of the pore space, the sample should always be scanned at the highest resolution permitted by the scanning instrument and the sample size. Results also suggest that the three segmentation methods recently developed for soil images thresholding are robust to different levels of noise as well as the choice of the 32-bit mapping interval, as long as lower and upper interval limits for mapping are chosen within suitable boundaries.
•Acquisition and reconstruction settings on soil image quality are evaluated.•Quantitative measures for image quality are refined and tested on soil images.•Three segmentation methods developed specifically for soil images are tested.•Recommendations on X-ray CT acquisition and reconstruction settings are provided.
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