Journal Paper Digests 2023 #22
- Long-term ecological research in freshwaters enabled by regional biodiversity collections, stable isotope analysis, and environmental informatics Get access Arrow
- Do carbon farming practices build bioavailable nitrogen pools?
- Combining ground penetrating radar methodologies enables large-scale mapping of soil horizon thickness and bulk density in boreal forests
- The appraisal of pedotransfer functions with legacy data; an example from southern Africa
- Interactions among soil texture, pore structure, and labile carbon influence soil carbon gains
- A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression
- Soil total suction sensing using fiber-optic technology
- Can we use X-ray CT to generate 3D penetration resistance data?
- Estimating plant-available nutrients with XRF sensors: Towards a versatile analysis tool for soil condition assessment
- Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities
Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities
The analysis of phospholipid fatty acids (PLFAs) is one of the most common methods used to quantify the abundance, and analyse the community structure, of soil microbes. The PLFA extraction method can yield two additional lipid fractions—neutral lipids and glycolipids—which potentially hold additional, valuable information on soil microbial communities. Yet its quantitative sensitivity on complete neutral lipid (NLFA) and glycolipid fatty acid (GLFA) profiles has never been validated. In this study we tested (i) if the high-throughput PLFA method can be expanded to concurrently extract complete NLFA and GLFA profiles, as well as sterols, (ii) whether taxonomic specificities of signature fatty acids are retained across the three lipid fractions in pure culture strains, and (iii) whether NLFAs and GLFAs allow soil-specific fingerprinting to the same extent as PLFA analysis. By adjusting the polarity of chloroform with 2% ethanol for solid phase extraction, pure lipid standards were fully fractionated into neutral lipids, glycolipids, and phospholipids. Sterols eluted in the neutral lipid fraction, and a betaine lipid co-eluted with phospholipids. We found consistent taxonomic specificities of fatty acid markers across the three lipid fractions by analysing pure culture extracts representative of soil microbes. Fatty acid profiles from soil extracts, however, showed stronger differences between PLFAs, NLFAs, and GLFAs than between soil types. This indicates that PLFAs and NLFAs signify different community properties (biomass vs. carbon storage, putatively), and that GLFAs are sensitive markers for community traits which behave differently than PLFAs. Although we consistently found high abundances of characteristic sterols in fungal extracts, the PLFA extraction method only yielded miniscule amounts of ergosterol from soil extracts. We argue that concomitant measurement of fatty acid profiles from all three lipid fractions is a low-effort and potentially information-rich addition to the PLFA method, and discuss its applicability for soil microbial community analyses.
Estimating plant-available nutrients with XRF sensors: Towards a versatile analysis tool for soil condition assessment
The timely diagnosis of plant-available soil nutrient contents is crucial in enhancing agricultural intensification and bridging yield gaps. There is a global demand for a practical and easy-to-use analytical tool capable of predicting the nutrient status of agricultural soils to make the soil chemical diagnosis faster, cheaper, and environmentally friendly. A growing body of research has highlighted the potential of energy dispersive X-ray fluorescence (XRF) sensors for monitoring the condition of agricultural soils. This study critically reviews current knowledge on the feasibility of using XRF sensors and suggests ways forward to predict plant-available soil nutrients. The review finds that some challenges need to be addressed, including: (i) mitigating the matrix effect in XRF spectral libraries and (ii) calibrating models that can capture the local context of the ratio between total and available nutrient content (T/A ratio). This study further discusses knowledge gaps related to the abovementioned challenges and proposes the following future research areas: (i) understanding the impact of soil management on the temporal stability of T/A ratio and XRF model performance; (ii) assessing advanced predictive modelling strategies to address the challenges related to XRF spectral libraries, i.e., to deal with matrix effect and local context of the relationship between total and available content of nutrients, and (iii) evaluating data acquisition and modelling strategies that optimize the in situ application of portable XRF. Understanding these points is critical to advancing the technological maturity of predicting available nutrients in situ to fulfil plant nutrient requirements along with its development. Finally, portable, easy-to-use analytical tools are key to enhancing soil health/condition monitoring and proposing best management practices in agricultural areas worldwide, particularly in regions with limited infrastructure of soil laboratories. Soil monitoring is critical to preserve, sustain and recover soil condition/health, one of the main manageable drivers of soil and food security.
Can we use X-ray CT to generate 3D penetration resistance data?
Noninvasive imaging of soils with X-ray CT has proven to be a useful method to assess soil structure from a pore space perspective. In contrast, methods like cone penetration tests reflect soil structure from the perspective of the soil matrix as assessed by its mechanical strength. Because both the gray value (GV) obtained with X-ray CT and the penetration resistance (PR) obtained with a cone penetration test depend on soil density there should be a relationship between the two. To the best of our knowledge, no studies attempted so far to investigate the nature of the PR ∼ GV relationship and to understand how well PR and GV are correlated. We aimed at bridging that gap and carried out a combined analysis of local GV and PR with undisturbed soil cores sampled in two soil textures, i.e., loam and sand. To carry out the GV measurements, we developed a new approach which considers an adaptive volume of the zone of influence of the penetrometer tip as a function of soil density. For sand and when looking at samples individually, the correlation between PR and GV was best when the soil microscale heterogeneity was high, i.e., when dense and loose zones of soil were present on the course of the penetrometer tip. For loam, the correlation between PR and GV was not dependent on soil heterogeneity. When looking at the whole dataset, the agreement between PR and GV was better in loam than in sand, with a distance correlation metric of 0.66 for loam and 0.34 for sand, respectively. For loam, the relationship PR ∼ GV had a trend which was similar to that of a hyperbola, i.e., with escalating PR values in a narrow GV range. For sand, no particular model could be recognized. In order to provide a proof-of-concept on how to generate 3D PR maps, the co-located measurements of GV and PR were used to establish an empirical relationship and X-ray CT was used to extrapolate it in 3D. This was carried out with the loam dataset by fitting a hyperbolic function to the PR ∼ GV data pairs. This model was then used to convert GVs into PR values, at a spatial resolution equal to that of the shaft diameter of the penetrometer tip we have used. Notwithstanding the fact that the suggested approach is dependent on numerous experimental conditions and edaphic factors, we advocate for the use of 3D PR maps. These maps could be used in root-soil interactions research, for which the study and breeding of cultivars that could show plastic response in their root systems under mechanical stress is becoming more and more important. This is particularly relevant in the context of mechanized modern agriculture.
Soil total suction sensing using fiber-optic technology
Measurement of the soil water suction is important for investigating geotechnical problems and mitigating associated risks; however, conventional high-range suction measurement techniques have some limitations for accurate, long-term, and quasi-distributed suction measurement in the field. Fiber-optic humidity sensors provide a viable solution due to their unique properties. Here, we present a novel microfabricated fiber-optic suction sensor for measuring the suction of water in unsaturated soils. We evaluated the performance of the sensor on four bentonite–sand mixtures by comparing it with two laboratory suction measurement methods (chill-mirror hygrometer, salt solution vapor equilibrium) and a capacitive relative humidity sensor. We determined that this sensor has a measurement range of 5–300 MPa with a root mean square error of less than 3.5 MPa, and a response time of 2–9 min over the tested suction variation range (6.5–80.5 MPa). The results of the evaporation model test showed that three fiber-optic sensors installed at different depths of bentonite could effectively capture changes in temperature, relative humidity in soil pores, and total suction during evaporation. Together, these results demonstrate the great potential of the fiber-optic suction sensor for in-situ, long-term, and quasi-distributed suction monitoring.
A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression
Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy.
Interactions among soil texture, pore structure, and labile carbon influence soil carbon gains
Perennial vegetation with high plant diversity, e.g., restored prairie, is known for stimulation of soil carbon (C) gains, due in part to enhanced formation of pore structure beneficial for long-term C storage. However, the prevalence of this phenomenon across soils of different types remains poorly understood. The aim of the study was to assess the associations between pore structure, soil C, and their differences in monoculture switchgrass and polyculture restored prairie vegetation across a wide range of soils dominating the Upper Midwest of the USA. Six experimental sites were sampled, representing three soil types with texture ranging from sandy to silt loams. The two vegetation systems studied at each site were (i) monoculture switchgrass (Panicum virgatum L.), and (ii) polyculture restored prairie, also containing switchgrass as one of its species. X-ray computed micro-tomography (µCT) was employed to analyze soil pore structure. Structural equation modeling and multiple path analyses were used to assess direct and indirect effects of soil texture and pore characteristics on microbial biomass C (MBC), particulate organic matter (POM), dissolved organic C (DOC), short-term respiration (CO2), and, ultimately, soil organic C (SOC). Across studied sites, prairie increased fractions of medium (50–150 µm Ø) pores by 11–45 %, SOC by 3–69 %, and MBC by 18–59 % (except for one site). The greater were the prairie-induced increases in the medium pore volumes, the greater were the prairie-induced SOC gains. Greater C losses via CO2 and DOC contributed to slower C accumulation in the prairie soil. We surmise that the interactive feedback loop relating medium pores and soil C acts across a wide range of soil textures and is an important mechanism through which perennial vegetation with high plant diversity, such as restored prairie, promotes rapid SOC gains.
The appraisal of pedotransfer functions with legacy data; an example from southern Africa
Predictions of soil hydraulic properties by pedotransfer functions (PTFs) must be treated with caution when they are used in an application domain which differs from the domain of their original development and calibration. However, in some settings, scientists may have little alternative but to use PTFs calibrated elsewhere. In this paper we consider how legacy data can be used to evaluate PTFs in new regions, paying particular attention to the challenges that arise when, as is often the case, the legacy data are not obtained by independent random sampling, and may be clustered at multiple scales. We undertook this work in southern Africa (Zimbabwe, Zambia and Malawi) where PTFs have been little-used, despite the scarcity of direct measurements of the soil properties of interest. We evaluated the extent to which existing PTFs provide a useful tool for the prediction of soil moisture content at field-capacity (−33 kPa) and permanent wilting-point (−1500 kPa) at different spatial scales. Soil legacy data for Zambia, Zimbabwe and Malawi were collated from various sources and PTFs from temperate and tropical domains were evaluated. We examined error variance components of predictions at within-profile, within-site and between-site scales; and estimated their mean errors. In general the better-performing PTFs (with respect to bias and the size of the error variance components) were ones calibrated with data from a tropical domain. This was most apparent at −1500 kPa. However, not all PTFs calibrated with data on tropical soils performed well, and predictions from some PTFs calibrated over a temperate domain were better at −33 kPa. The observations were spatially clustered, with data from different depth intervals in the same profile, from profiles in the same experimental site or farm, and from clusters across the region. This enabled us to show, with an appropriate mixed model analysis, that PTFs which effectively capture regional-scale variation may be less useful for predicting variation within a profile. We propose that such studies, based on legacy data, and with a suitable linear mixed model, should be used to screen PTFs of any provenance before their wider application.
Combining ground penetrating radar methodologies enables large-scale mapping of soil horizon thickness and bulk density in boreal forests
Forest soil properties must be observed with the appropriate resolution by depth and landscape area to understand biogeomorphological controls on soil carbon (C). These observations, particularly in boreal forests, have been limited because of the poor resolution and unavailability of physical soil sampling results, especially for soil bulk density measurements. Ground penetrating radar (GPR) has been demonstrated to non-destructively and continuously estimate forest soil properties required in Cstock estimates, such as soil horizon thickness and soil bulk density, across small spatial scales and shallow depths. Yet, successful small-scale forest GPR approaches represent a potential opportunity to obtain soil property estimates at relevant resolution and depth across forest landscapes, enabling improvement to much needed soil mapping and stock estimates. This review discusses the existing soil property studies that utilize ground penetrating radar (GPR) and explores how the adaptation of GPR methodology can contribute to investigating soils in forest landscapes. We have identified common GPR surveying practices, data processing steps and interpretation methods employed in multiple studies. These approaches have proven effective in obtaining higher-resolution estimates of important soil properties, such as bulk density and horizon thickness, within small-scale forest plots. By applying relevant findings in this review to our own boreal forest investigation across an 80 m hillslope transect, we provide recommendations on how to tailor GPR methodology for landscape-scale estimates of soil horizon thickness and bulk density to examine forest soil property distribution. These findings should enable the future collection of soil datasets informing the distribution of soil C stocks and their relationship to landscape features, and thus their controls and responses to climate and environmental change.
Do carbon farming practices build bioavailable nitrogen pools?
Agricultural soils contain large amounts of nitrogen (N), but only a small fraction is readily available to plants. Despite several methods developed to estimate the bioavailability of N, there is no consensus on which extraction methods to use, and which N pools are critically important. In this study, we measured six soil N pools from 20 farms, which were part of a multi-year soil carbon sequestration on-farm experiment (Carbon action, 2019–2023). The aim was to quantify the N pools and to evaluate if farming practices that aim to build soil carbon pools, also build bioavailable N pools. We also aimed to test if the smaller and rapidly changing N pools could serve as an indicator for the slower change in soil organic matter. The measured N pools decreased in size, when moving from total N (7700 ± 1500 kg/ha) to slowly cycling (Illinois Soil Nitrogen Test ISNT-N: 1063 ± 220 kg/ha, autoclave citrate-extracted ACE protein N: 633 ± 440 kg/ha), water-soluble organic N (50 ± 17 kg/ha), potentially mineralizable N (33 ± 13 kg/ha) and finally readily plant available inorganic pools (nitrate and ammonium, total: 14 ± 8 kg/ha). In total, the measured pools covered only 18%–44% of total N, indicating a large unidentified N pool, which is either tightly bound to soil mineral fraction and not easily extractable or is bound to undecomposed plant residues and not hydrolysed by the methods. Of the large N pools (ISNT-N, ACE protein and unidentified residual N), clay, carbon (C) and C:Clay ratios explained most of the variability (R2 = .90–.93), leaving a minor part of the variation to the management effect. A pairwise comparison of carbon farming and control plots concluded that farming practices had a small (3%–5%) but statistically significant (p < .05) effect on soil total N and ISNT-N pools, and a moderate and significant effect (18%, p < .01) on potentially mineralizable N. The large variation in protein N, water-soluble organic N and inorganic N reduced statistical significance, although individual C sequestration practices had large effects (−30% to +50%). In conclusion, carbon sequestration practices can build both slowly cycling N pools (ISNT) and increase the mineralisation rate of these pools to release plant available forms, resulting in an additional benefit to agriculture through reduced fertilizer application needs.
Long-term ecological research in freshwaters enabled by regional biodiversity collections, stable isotope analysis, and environmental informatics Get access Arrow
Biodiversity collections are experiencing a renaissance fueled by the intersection of informatics, emerging technologies, and the extended use and interpretation of specimens and archived databases. In this article, we explore the potential for transformative research in ecology integrating biodiversity collections, stable isotope analysis (SIA), and environmental informatics. Like genomic DNA, SIA provides a common currency interpreted in the context of biogeochemical principles. Integration of SIA data across collections allows for evaluation of long-term ecological change at local to continental scales. Challenges including the analysis of sparse samples, a lack of information about baseline isotopic composition, and the effects of preservation remain, but none of these challenges is insurmountable. The proposed research framework interfaces with existing databases and observatories to provide benchmarks for retrospective studies and ecological forecasting. Collections and SIA add historical context to fundamental questions in freshwater ecological research, reference points for ecosystem monitoring, and a means of quantitative assessment for ecosystem restoration.