Journal Paper Digests

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Journal Paper Digests 2023 #20

  • A new concept for modelling the moisture dependence of heterotrophic soil respiration
  • Depth-dependent driver of global soil carbon turnover times
  • Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions
  • Enhanced carbon storage in semi-arid soils through termite activity
  • Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed
  • Modelling opportunities of potential European abandoned farmland to contribute to environmental policy targets
  • Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content
  • Soil dielectric permittivity modelling for 50 MHz instrumentation
  • Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries
  • A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

Soil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core-scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core-scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.

Failure to scale in digital agronomy: An analysis of site-specific nutrient management decision-support tools in developing countries

While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where “scaling” is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5–10 % of the target farmers compared with reaching none, decreases by ∼200% when a technical problem is stated as a reason for the tools’ failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional ‘blanket’ fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled ‘ready’ from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.

Soil dielectric permittivity modelling for 50 MHz instrumentation

Near surface electromagnetic geophysical techniques are proven tools to support soil ecosystem services and soil exploration. Such geophysical techniques provide electromagnetic properties that are useful to characterize the studied soil. The link between relevant soil characteristics and geophysical properties, such as dielectric permittivity (ε), is commonly expressed by pedophysical models. However, some weaknesses remain in their application, such as the requirement of parameters that are difficult to measure or calculate. Therefore, these parameters are frequently fixed, but this oversimplifies the complexity of the investigated soils. Moreover, the validity of ε pedophysical models in the frequency range of operating soil moisture sensors (normally < 100 MHz) remains poorly investigated.

Specific surface area of soils with different clay mineralogy can be estimated from a single hygroscopic water content

The soil specific surface area (SSA) is an important variable for soil science and geoenvironmental engineering applications, but traditional measurement methods are difficult and time-consuming. Regression models or pedotransfer functions are often used to estimate SSA from other soil properties (e.g., clay content and cation exchange capacity), but these models do not consider the impact of clay mineralogy. Hygroscopic water content (wh) is intimately linked to these soil properties, which suggests that wh may be a better parameter for SSA estimation. This study (i) proposes regression models that estimate SSA from wh at different relative humidity values (5 to 90%) for kaolinite-rich samples (KA), illite-rich or mixed clay samples (IL/MC), montmorillonite-rich samples (ML), and a combination of all samples (ALL) and (ii) compares the performance of the wh models to other published models that comprise clay, silt and soil organic carbon contents and cation exchange capacity. We found that the sample-specific wh regression models accurately estimated SSA for KA, IL/MC and ML samples. For KA and IL/MC samples, the performance of the KA model (e.g., for adsorption, average RMSE = 10.5 m2/g) and IL/MC model (average RMSE = 21.3 m2/g) were better than the ALL-calibration model (KA: average RMSE = 18.7 m2/g; ML: average RMSE = 22.4 m2/g). For ML samples, similar model performance between the ML-calibration model (average RMSE = 41.4 m2/g) and the ALL-calibration model (average RMSE = 41.1 m2/g) was observed. In addition, the model performance of regression models based on wh was superior to models published in the literature that are based on clay, silt and soil organic carbon contents and cation exchange capacity. Overall, this study confirms that a single measure of wh can provide reliable estimates of the SSA while revealing a significant impact of clay mineralogy on model performance.

Modelling opportunities of potential European abandoned farmland to contribute to environmental policy targets

Farmland abandonment is a major proximate driver of landscape change in European rural areas and is often followed by natural revegetation. In certain conditions, it might be preferable to prevent or reverse farmland abandonment or manage these areas towards active restoration (i.e., guided rewilding with wild or domesticated animals). These alternative responses to farmland abandonment lead to context-dependent impacts, which can potentially contribute to European Green Deal objectives for environment and rural areas. While previous studies analysed direct impacts of abandonment, there is little insight into how alternative ways of managing abandoned farmland can best contribute to environmental policy goals, and what type of management is preferred where. To assess opportunities in these areas, we compared three abandonment trajectories: natural revegetation, active restoration with rewilding, and extensive re-farming. We analysed the potential positive and negative environmental and cultural impacts of developing these management strategies in all farmland locations that could potentially be abandoned across Europe. Mapping and quantification of the benefits and risks associated with different management responses to abandonment indicate a large spatial variation across regions. While natural revegetation can support high benefits for carbon sequestration and erosion reduction, it is also linked to more frequent trade-offs than re-farming and rewilding. However, there is a very strong spatial variation in these trade-offs. It is worthwhile to focus on areas with the largest gains and fewest trade-offs when targeting investments for prevention of abandonment or rewilding. Our maps can help inform interventions in abandoned farmland to maximise the potential contributions of these lands to the European Green Deal environmental and rural policy targets.

Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed

Many spatial modeling methods have emerged; however, they require dependent variables, cannot reflect the relationship between categorical variables and numerical variables, and are limited by the interference of data collinearity. Categorical principal components analysis (CATPCA) has the potential to overcome these issues. Therefore, in order to investigate the suitability of CATPCA for spatial modeling, we conducted a case study based on the generation susceptibility of gully heads was determined, including 2310 gully head and 23 variables. CATPCA was first used in the spatial modeling of gully head generation. The first six principal components retained 76.4% of data trends. The area of the training and validation sensitivity curves were 75.4% and 75.7%, respectively, which reflected good levels. CATPCA can simulate gully head spatial differences, and thus, has great potential for spatial modeling applications. Among the 23 factors, elevation, distance to residential areas, human footprint, lithology, and soil type were identified as the main controlling factors affecting the generation susceptibility of gully heads, with high correlations between them. Low altitudes, close proximity to residential areas, high human footprint, and poor vegetation were associated with high susceptibility. The findings of this study provide a better understanding of the applicability of CATPCA for spatial modeling. CATPCA is a novel solution for spatial modeling strategies that can improve understanding and has great potential for various spatial modeling applications in the future.

Enhanced carbon storage in semi-arid soils through termite activity

Termites are keystone species in natural ecosystems and their role in the C cycle is potentially substantial but poorly understood. Large (20–40 m) mounds (heuweltjies) of the harvester termite Microhodotermes viator occupy up to a quarter of the semi-arid west coast region of South Africa but their C storage potential is unknown. This study determined the organic and inorganic C fractions, C stocks, and their correlation with each other, depth, and biogenic features in these mounds. Trenches (30–60 m) were excavated through 3 mounds: Buffels River (m.a.p < 100 mm), Klawer (m.a.p 100–200 mm) and Piketberg (m.a.p 300–400 mm) and grid sampled. Mound soils had significantly higher soil organic carbon (SOC) and inorganic carbon (SIC) than surrounding soils. Total C was strongly correlated (ρ > 0.9; p < 0.001) with SIC in the arid mounds and SOC (ρ > 0.75; p < 0.001) in the higher rainfall mound. There was no consistent relationship between SOC and SIC distributions throughout the mounds, which is likely related to solubility-linked translocations of carbonates. For all mounds, SOC was highest in topsoils with a second clear peak in subsoils (>1 m) that was associated with biogenic features, termite channels and burrows. Subsoils contributed substantially (36–41 %) to the total C stock. Total C stocks for the intermediate rainfall mound (Klawer) were estimated at 14.6 tons per mound, with 1.1 tons SOC. In this region, mounds occupy 27 % of the total area but contribute 44 % of the total SOC stock to a depth of 80 cm. This highlights the disproportionate contribution termite mounds make to carbon stocks of these semi-arid environments and demonstrates the importance of deep (<1 m) soil carbon for C modelling. Termite activity needs to be recognized as a major contributor to C stock variability both laterally and at depth and accounted for in land-use change (CO2-LULUCF) models.

Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions

Predicting soil properties and evaluating their functions along with their related driving factors is useful for providing useful geographical information for soil management, which is especially important in arid and semi-arid regions. This study investigates the use of a structural equation modeling (SEM) approach for assessing the effects of soil forming factors through environ-mental proxies on three key soil properties, namely soil organic carbon (SOC), calcium carbonate equivalent (CCE) and clay content (clay) in an arid and semi-arid region of Iran. Using a set of 259 soil profiles collected over years 2016–2020 in the Qazvin plain, the cause-effect relationships were estimated between these soil properties and nine environmental factors derived from a digital elevation model and from satellite images. Focusing on two main horizons A and B, it was shown that normalized difference vegetation index, midslope position, elevation, multi-resolution valley bottom flatness, and saga wetness index are impacting these soil properties. Inside each horizon, the effect of CCE and clay on SOC was also evidenced, but to an extent that depends on the horizon. For each soil property, we were able to clearly identify the relationships between the two horizons. Although our SEM approach proved to be useful for identifying and estimating the cause-effect relationships, it failed to provide a good predictive model as required for a relevant digital soil mapping of these soil properties. However, as the SEM approach allows combining soil science knowledge inside a model that accounts for soil forming factors, external factors, and soil system at the same time, it permits an investigation of their potential cause-effect relationships in a rich theoretical framework. The SEM methodology is thus potentially useful for soil scientists that are studying various soil properties in other parts of the world, even if it cannot be advocated as an efficient digital soil mapping method in general.

A new concept for modelling the moisture dependence of heterotrophic soil respiration

The moisture dependence of heterotrophic soil respiration is a key factor affecting the uncertainty in predicting the response of soil organic carbon (SOC) to global warming. Considering that heterotrophic respiration from unsaturated soils is primarily driven by microbial reduction of oxygen (O2), we propose a new concept to model the respiration by tracking dissolution of gaseous O2 and its subsequent diffusion and microbial reduction at hydrated microsite in the pore space of soil. Total respiration from a soil sample is calculated by summing the O2 reduced by all microbes in the soil. This allows us to separate physical processes and microbial activity occurring at microsites and incorporate pore-scale substrate heterogeneity, macropores and other factors explicitly into the model. We show that scaling up these microscopic physical processes over a soil sample makes soil moisture, temperature, and other factors inherently integrated in their influence on microbial respiration, and that a change in one of them affects the response of the respiration to the change in others. Comparison with experimental data shows the model can reproduce the diverse moisture-respiration relationships observed from various experiments and predict the change in soil respiration with temperature. It is noteworthy to point out that previous studies had attributed the variations in the moisture and temperature sensitivity of heterotrophic soil respiration to microbial adaptation; herein we demonstrate that changes in soil structure and physical processes can also give rise to such variations. Distinguishing between physical and microbial effects in data analysis and modelling is therefore crucial, as mistaking physical effects for microbial adaptation would lead to errors in predicting the response of SOC to environmental changes.

Depth-dependent driver of global soil carbon turnover times

Soil carbon fixation has the potential to offset anthropogenic carbon emissions and mitigate climate change. However, the carbon fixation capacity still remains uncertain at the global scale, and little is known about the patterns and controls of soil carbon turnover times. Here we synthesize 5188 radiocarbon measurements at the global scale, and random forest models are applied to assess the key drivers of soil carbon turnover times in different soil layers. We find that across the globe, the mean soil carbon turnover time (τ) is 4178 ± 106 years (mean ± standard error), but the turnover time varies significantly across different regions and land cover types, with the longest values of τ being observed in tundra and the shortest in temperate forests. Longer soil carbon turnover times are observed in the northern permafrost regions, where the mean τ value is nearly twice that of the non-permafrost regions. Furthermore, τ is generally longer in subsoil than that in topsoil across all ecosystems. Moreover, we find the key drivers of τ are depth-dependent. The most important factors affecting topsoil τ are microbes (bacteria, fungi), while soil mineral protection is the major contributor to subsoil τ. These results highlight the necessity to integrate depth-specific soil carbon turnover time and its associated drivers in carbon cycling models into future climate change scenarios.

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