Understanding Of Soil Processes Research Articles

The potential of enhanced phytoremediation to clean up multi-contaminated soil – insights from metatranscriptomics

This study aimed to (i) investigate the potential for enhanced phytoremediation to remove contaminants from soil historically co-contaminated with petroleum hydrocarbons (PHs) and heavy metals (HMs) and (ii) analyze the expression of crucial bacterial genes and whole metatranscriptomics profiles for better understanding of soil processes during applied treatment. Phytoremediation was performed using Zea mays and supported by the Pseudomonas qingdaonensis ZCR6 strain and a natural biofertilizer: meat and bone meal (MBM). In previous investigations, mechanisms supporting plant growth and PH degradation were described in the ZCR6 strain. Here, ZCR6 survived in the soil throughout the experiment, but the efficacy of PH removal from all soils fertilized with MBM reached 32 % regardless of the bacterial inoculation. All experimental groups contained 2 % (w/w) MBM. The toxic effect of this amendment on plants was detected 30 days after germination, irrespective of ZCR6 inoculation. Among the 17 genes tested using the qPCR method, only expression of the acdS gene, encoding 1-aminocyclopropane-1-carboxylic acid deaminase, and the CYP153 gene, encoding cytochrome P450-type alkane hydroxylase, was detected in soils. Metatranscriptomic analysis of soils indicated increased expression of methane particulated ammonia monooxygenase subunit A (pmoA-amoA) by Nitrosomonadales bacteria in all soils enriched with MBM compared to the non-fertilized control. We suggest that the addition of 2 % (w/w) MBM caused the toxic effect on plants via the rapid release of ammonia, and this led to high pmoA-amoA expression. In parallel, due to its wide substrate specificity, enhanced bacterial hydrocarbon removal in MBM-treated soils was observed. The metatranscriptomic results indicate that MBM application should be considered to improve bioremediation of soils polluted with PHs rather than phytoremediation. However, lower concentrations of MBM could be considered for phytoremediation enhancement. From a broader perspective, these results indicated the superior capability of metatranscriptomics to investigate the microbial mechanisms driving various bioremediation techniques.

Soil metabolomics - current challenges and future perspectives

Soil is an extremely complex and dynamic matrix, in part, due to the wide diversity of organisms living within it. Soil organic matter (SOM) is the fundamental substrate on which the delivery of ecosystem services depends, providing the metabolic fuel to drive soil function. As such, studying the soil metabolome (the diversity and concentration of low molecular weight metabolites), as a subset of SOM, holds the potential to greatly expand our understanding of the behaviour, fate, interaction and functional significance of small organic molecules in soil. Encompassing a wide range of chemical classes (including amino acids, peptides, lipids and carbohydrates) and a large number of individual molecules (ca. n = 105 to 106), the metabolome is a resultant (indirect) output of several layers of a biological hierarchy, namely the metagenome, metatranscriptome and metaproteome. As such, it may also provide support and validation for these “multi-omics” datasets. We present a case for the increased use of untargeted metabolomics in soil biochemistry, particularly for furthering our fundamental understanding of the functions driving SOM composition and biogeochemical cycling. Further, we discuss the scale of the challenge in terms of metabolite extraction, analysis and interpretation in complex plant-soil-microbial systems. Lastly, we highlight key knowledge gaps which currently limit our use of metabolomic approaches to better understand soil processes, including: (i) interpretation of large untargeted metabolomic datasets; (ii) the source, emission and fate of soil-derived volatile organic compounds (VOCs), (iii) assessing temporal fluxes of metabolites, and (iv) monitoring ecological interactions in the rhizosphere. While the application of metabolomics in ecosystem science is still in its relative infancy, its importance in understanding the biochemical system in relation to regulation, management and underpinning the delivery of ecosystem services is key to further elucidating the complex links between organisms, as well as the fundamental ability of the biological community to process and cycle key nutrients.

Do we speak one language on the way to sustainable soil management in Europe? A terminology check via an EU‐wide survey

AbstractEuropean soils are under increasing pressure, making it difficult to maintain the provision of soil ecosystem services (SESs). A better understanding of soil processes is needed to counteract soil threats (STs) and to promote sustainable soil management. The EJP SOIL programme of the EU provides a framework for the necessary research. However, different definitions of soil‐related terms potentially lead to varied understandings of concepts. Furthermore, there are numerous indicators available to quantify STs or SESs. As unclear communication is a key barrier that hinders the implementation of research results into practice, this study aimed to answer the question about whether the terminology of large‐scale initiatives is adequately understood within the soil‐science community and non‐research stakeholders. An online questionnaire was used to provide definitions for 33 soil‐related terms in both scientific and plain language, as well as indicators for seven SESs and 11 STs. Participants were asked to rate their agreement with the definitions and indicators on a seven‐grade Likert scale. The level of agreement was calculated as the percentage of ratings above 4, the neutral position. The survey was available from June to September 2023 and was distributed by a snowball approach. More than 260 stakeholders assessed the survey; 70% of respondents were researchers, and 15% were practitioners. Mean agreement levels for the definitions and indicators were generally high, at 85% and 78% respectively. However, it was apparent that the lowest agreement was found for terms that are relatively new, such as Ecosystem Services and Bundle, or unfamiliar for certain subgroups, such as ecological terms for stakeholders working at the farm scale. Due to their distinct majority, the results of this study primarily reflect the opinions of scientists. Thus, broad conclusions can only be drawn by comparing scientists with non‐scientists. In this regard, the agreement was surprisingly high across all types of questions. The combined outcomes indicate that there is still a need to facilitate communication between stakeholders and to improve knowledge distribution strategies. Nevertheless, this study can support and be used by future projects and programmes, especially regarding the harmonization of terminology and methods.

Appraisal of Visible/IR and microwave datasets for land surface fluxes estimation using machine learning techniques

Land surface fluxes such as Soil Moisture (SM) and Soil Temperature (ST) are very important variables for many applications that includes agriculture water management, weather and climate prediction, natural disasters etc. Further, they are important for understanding soil processes, hydrological balances as well as changes in microbial population. Mapping of the soil moisture content at various depth is crucial for the sustenance of water resources and also to understand about the development of crops in forms of quality and yield. With changing environmental conditions, there is a need of approaches for estimating SM and ST in various climatic and geographic situations. Towards this, Earth Observation datasets at higher resolutions from satellites such as Sentinel 1 and 2, could play an important role in the monitoring of SM and ST over the larger areas. For estimation of SM and ST, machine learning approaches could be effective. This research looked into the possibilities of using Earth Observation (EO) data of Sentinel-1 (S1) and Sentinel-2 (S2) simultaneously to estimate SM and ST by using the machine learning methods such as random forest (RF) and Support Vector Machines (SVM). The coefficient of correlation (r), root mean square error (RMSE), and Bias are utilized in model enactment for accuracy and comparative analysis of the models used. The overall analysis indicates that the SVM model (r = 0.85, RMSE = 2.54, Bias = −0.05) is the second most appropriate after the RF model (r = 0.89, RMSE = 2.34, Bias = 0) for estimating land surface fluxes (SM and ST).

Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations

Digital soil mapping (DSM) is commonly conducted using input soil attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source of soil data that has the potential to enhance DSM predictions. However, they are not widely used due to to concerns about subjectivity and data quality. This study investigates the usefulness of hand-feel soil texture (HFST) data for DSM. We processed HFST data obtained from forest soils in France from two inventory campaigns: (i) HFST determination from systematic 1 km2 grid observations in France utilizing a specialized soil texture triangle, and (ii) HFST observations from soil survey samples, using a different texture triangle. Both sets of HFST data were used as input soil variables, with the same covariates, for predicting topsoil texture In a sizable, forested area through a DSM method. By employing independent sampling and laboratory soil analyses in selected areas, we uncovered measurement bias in one of the datasets. However, intriguingly, these biased observations identified subtle yet highly specific and unexpected patterns of sands in terraces due to alluvial deposits along small rivers. Thus, field soil observations, even if they are biased, should not be dismissed solely based on their overall predictive performance. It is essential to carefully examine predicted maps and covariates to determine whether patterns may have pedological and/or lithological origins and if they are pertinent for enhancing DSM predictions, enhancing soil process understanding, and meeting the requirements of end users. Numerous HFST are available worldwide, these datasets are usually disregarded for DSM. Here we contend that efforts should be put in recovering these data, and their potential for enhancing DSM and deepening our understanding of soil processes.

Remote Sensing Monitoring of Durum Wheat under No Tillage Practices by Means of Spectral Indices Interpretation: A Preliminary Study

Due to its advantages, remote sensing monitoring has been used in various applications and made noteworthy contributions to understanding soil and plant processes, as well as in the agriculture sector. The aim of the work is to compare the return of durum wheat crops in conservative agricultural practices in Mediterranean climate conditions by analysing the data from the Sentinel2 satellite through three spectral indices. The analysed spectral indices have different interpretations and therefore have been studied in different periods: (i) NDVI (normalized difference vegetation index) for the evaluation of the vegetative vigour from January to June; (ii) NDWI (normalized difference water index) for the moisture of covered soil from January to June and of bare soil after harvesting from June to August; and (iii) NMDI (normalized multi-band drought index) for the variability of bare soil moisture from June to August. With reference to the machines used in cultivation practices, a further purpose of the study is to investigate the effects of automatic guidance versus manual guidance on production yields and on the spectral indices considered. The first results show that the NDVI follows crop phenological stages by reaching the maximum values in correspondence with the stem elongation and booting stages. Additionally, the NDWI showed the same trend as the NDVI during the current crop. After harvesting, the NDWI showed higher values in the plots cultivated under conservation tillage practices. In the same period, the NMDI showed the same results as the NDWI and a positive correlation, confirming that tillage practices could imply a lower ability to retain water in drought time.

Long Term Soil Gas Monitoring as Tool to Understand Soil Processes

Soils provide many functions as they represent a habitat for flora and fauna, supply water, nutrient, and anchorage for plant growth and more. They can also be considered as large bioreactors in which many processes occur that involve the consumption and production of different gas species. Soils can be a source and sink for greenhouse gases. During the last decades this topic attracted special attention. Most studies on soil-atmosphere gas fluxes used chamber methods or micro-meteorological methods. Soil gas fluxes can also be calculated from vertical soil gas profiles which can provide additional insights into the underlying processes. We present a design for sampling and measuring soil gas concentration profiles that was developed to facilitate long term monitoring. Long term monitoring requires minimization of the impact of repeated measurements on the plot and also minimization of the routine workload while the quality of the measurement needs to be maintained continuously high. We used permanently installed gas wells that allowed passive gas sampling at different depths. Soil gas monitoring set ups were installed on 13 plots at 6 forest sites in South West Germany between 1998 and 2010. Until now, soil gas was sampled monthly and analysed for CO2, N2O, CH4, O2, N2, Ar, and C2H4 using gas chromatography. We present typical time series and profiles of soil gas concentrations and fluxes of a selected site as an example. We discuss the effect of different calculation approaches and conclude that flux estimates of O2, CO2 and CH4 can be considered as highly reliable, whereas N2O flux estimates include a higher uncertainty. We point out the potential of the data and suggest ideas for future research questions for which soil gas monitoring would provide the ideal data basis. Combining and linking the soil gas data with additional environmental data promises new insights and understanding of soil processes.

Stratified Data Reconstruction and Spatial Pattern Analyses of Soil Bulk Density in the Northern Grasslands of China

The spatial pattern of soil bulk density in the grasslands of northern China largely remains undefined, which raised uncertainty in understanding and modeling various soil processes in large spatial scale. Based on the measured data of soil bulk density available from soil survey reports from the grasslands of northern China, we constructed a soil Stratified Pedotransfer function (SPTF) from the surface soil bulk density. Accordingly, the stratified bulk density data of soil vertical profile was reconstructed, and the estimation of soil bulk density data in horizontal space was performed. The results demonstrated that the soil bulk density of the grasslands of northern China was typically high in the central and northwestern regions and low in the eastern and mountainous regions. Mean soil bulk density of the grasslands was 1.52 g·cm−3. According to geographical divisions, the highest soil bulk density was observed in the Tarim basin, with mean soil bulk density of 1.91 g·cm−3. Conversely, the lowest soil bulk density was observed in the Tianshan Mountain area, with mean soil bulk density of 1.01 g·cm−3. Based on data obtained on various types of grasslands, the soil bulk density of alpine meadow was the lowest, with a mean soil bulk density of 0.75 g·cm−3, whereas that of temperate desert was the highest, with mean soil bulk density of 1.80 g·cm−3. Mean prediction error, root mean square deviation, relative error, and multiple correlation coefficient of soil bulk density data pertaining to surface layer (0–10 cm) in the grasslands of northern China were 0.018, 0.223, 16.2%, and 0.5386, respectively. The approach of employing multiple data sources via soil transfer function improved the estimation accuracy of soil bulk density from stratified soils data at the large scale. Our study would promote the accurate assessment of grassland carbon storage and fine land characteristics mapping.

Soil Carbon Dynamics under Pastures in Andean Socio-Ecosystems of Colombia

Models can help to explain the main interactions, magnitudes, and velocity by which biological processes accumulate soil organic carbon (SOC) in pastures. An explanatory model using Insight Maker software was constructed for each soil under natural and cultivated pastures, using theoretical carbon models and data which were collected monthly in andisol sites. The model was calibrated and validated by comparing the modeled data to the field data until the smallest prediction error was reached. The indicators used were the mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE) and the coefficient of determination (R2). In natural pasture soil, the diversification of organic inputs consistently promoted the growth of microbial biomass and metabolic efficiency. In contrast, intensive management of cultivated pastures, involving the removal of plant cover, plowing and low input of organic matter, stressed the microbial community and increased the potential carbon loss through secondary mineralization and surface runoff. The application of modeling indicated that it is necessary to improve agronomic practices in cultivated pastures, to maintain soil cover and to increase the application of organic fertilizer by 1.5 times, in order to reduce stress on the microbial biomass, accumulate SOC, minimize organic matter mineralization and reduce C losses due to surface runoff. Therefore, improving agricultural management based on the understanding of soil processes will allow increasing the potential for SOC storage, while improving pasture sustainability.

Soil Science Challenges in a New Era: A Transdisciplinary Overview of Relevant Topics

Transdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Research.

Application of a Cellular Automaton Method to Model the Structure Formation in Soils Under Saturated Conditions: A Mechanistic Approach

Soil functions are closely related to the structure of soil microaggregates. Yet, the mechanisms controlling the establishment of soil structure are diverse and partly unknown. Hence, the understanding of soil processes and functions requires the connection of the concepts on the formation and consolidation of soil structural elements across scales that are hard to observe experimentally. At the bottom level, the dynamics of microaggregate development and restructuring build the basis for transport phenomena at the continuum scale. By modeling the interactions of specific minerals and/or organic matter, we aim to identify the mechanisms that control the evolution of structure and establishment of stationary aggregate properties. We present a mechanistic framework based on a cellular automaton model to simulate the interplay between the prototypic building units of soil microaggregates quartz, goethite, and illite subject to attractive and repulsive electrostatic interaction forces. The resulting structures are quantified by morphological measures. We investigated shielding effects due to charge neutralisation and the aggregate growth rate in response to the net system charge. We found that the fraction as well as the size of the interacting oppositely charged constituents control the size, shape, and amount of occurring aggregates. Furthermore, the concentration in terms of the liquid solid ratio has been shown to increase the aggregation rate. We further adopt the model for an assessment of the temporal evolution of aggregate formation due to successive formation of particle dimers at early stages in comparison to higher order aggregates at later stages. With that we show the effect of composition, charge, size ratio, time and concentration on microaggregate formation by the application of a mechanistic model which also provides predictions for soil aggregation behaviour in case an observation is inhibited by experimental limitations.

Can the comparison of above- and below-ground litter decomposition improve our understanding of bacterial and fungal successions?

The relationship between litter quality and life strategy of soil microorganisms (copiotrophy vs oligotrophy) is important for understanding soil processes such as decomposition. Yet, whether and how this relationship may vary with the addition of substrates of contrasting quality (i.e., labile vs recalcitrant) has rarely been evaluated for both bacteria and fungi simultaneously. Using a 3-month incubation experiment with either maize leaves (enriched in soluble carbon (C)) or roots (enriched in structural C), we measured changes in litter quality in association with the composition of bacterial and fungal communities assessed via pyrosequencing after 0, 15, 35 and 91 days. Overall, leaf addition led to a higher differentiation from the unamended soil for bacterial and early-decomposers fungal communities compared with root addition. This finding clearly indicates that the differentiation of microbial communities strongly depends on substrate quality for both bacterial and fungal communities. Further, the differentiation of bacterial communities after litter addition remained relatively similar throughout the incubation period. This suggests that many bacterial taxa are more adapted to complex C compounds than previously thought. Finally, our study underscores the limits of the copiotroph–oligotroph model at the phylum level and the necessity to work at a finer taxonomic resolution.

Sub-micrometer distribution of Fe oxides and organic matter in Podzol horizons

The spatial distribution of soil constituents at the micrometer scale is of great importance to understand processes controlling the formation of micro-aggregates and the stabilization of organic carbon. Here, the spatial distribution of organic and mineral constituents in Podzol horizons is studied by concerted measurements of (i) the content of various forms of Fe, Al, Si and C determined by selective extraction in the fine earth fraction of soil (f < 2 mm); (ii) the elemental composition of the clay fraction (f < 2 μm) with lateral resolution using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and with surface selectivity using X-ray photoelectron spectroscopy (XPS); (iii) the specific surface area (SSA) of fine earth and clay fractions by krypton physisorption.The SSA of the fine earth in illuvial horizons is predominantly due to finely divided Fe oxides, including goethite, characterized by an equivalent particle size of about 10 nm. Kaolinite platelets of about 2 μm size account for a large volume proportion in the clay fraction but have a minor contribution to SSA. Fe oxides and organic matter (OM) are intimately associated. Heterogeneity at the μm scale is created by local variations in the relative amounts of kaolinite and Fe-OM associations. These two kinds of physical entities are in random mixture. Moreover, variation of C/Fe atomic ratios reveals sub-μm scale heterogeneity. The latter is due to variation in the relative proportion of organic compounds and Fe oxides, indicating that aggregation of nanoparticles, and not only mere adsorption or pore filling, plays a role in these associations. In this regard, our results highlight that OM associated with Fe protects Fe oxides against physical displacement and that part of this associated OM is oxidizable by NaOCl treatment. These findings demonstrate that the concept of OM stabilization through association with Fe must be revisited when considering the sub-μm scale level because fine Fe oxide particles can be easily dispersed during oxidation of associated carbon. Combination of physical fractionation and microanalysis (e.g. SEM-EDS, vibrational spectroscopy) offer promising perspectives to clarify the relationship between chemical composition and sub-μm scale architecture, and to better understand soil processes.

Soil microbial processes and resource limitation in karst and non‐karst forests

Abstract Soil micro‐organisms play a key role in soil biogeochemical cycles, but their growth and activities are often limited by resource availability. Understanding soil processes that are driven by micro‐organisms and resource limitation of microbes will help to elucidate controls on soil fertility and improve the ability to predict the responses of an ecosystem to global changes. As a widespread ecosystem type, karst ecosystem develops from limestone or dolomite with unique soil; however, karst ecosystems remain poorly understood regarding their soil microbial processes and microbial resource limitation. Here, ecoenzymatic stoichiometry was used as an indicator of microbial resource limitation, and to model major microbial processes (i.e. decomposition of soil organic carbon and microbial respiration) in a karst and a non‐karst forest. Results showed that the modelled decomposition and respiration rates were significantly higher in the karst forest than in the non‐karst forest. In addition, results of ecoenzymatic stoichiometry showed that the karst forest was more carbon‐limited than the non‐karst forest. In contrast, the karst forest was likely saturated with nitrogen, but the non‐karst forest was limited by nitrogen. Both the karst and non‐karst forests were limited by phosphorus, but phosphorus deficiency was more evident in the non‐karst forest than in the karst forest. These findings highlight the specific profiles of karst ecosystems, and they suggest that the responses of karst ecosystems to global changes should be very different compared to other ecosystems. A plain language summary is available for this article.

Soil chemistry changes beneath decomposing cadavers over a one-year period

Decomposing vertebrate cadavers release large, localized inputs of nutrients. These temporally limited resource patches affect nutrient cycling and soil organisms. The impact of decomposing cadavers on soil chemistry is relevant to soil biology, as a natural disturbance, and forensic science, to estimate the postmortem interval. However, cadaver impacts on soils are rarely studied, making it difficult to identify common patterns.We investigated the effects of decomposing pig cadavers (Sus scrofa domesticus) on soil chemistry (pH, ammonium, nitrate, nitrogen, phosphorous, potassium and carbon) over a one-year period in a spruce-dominant forest. Four treatments were applied, each with five replicates: two treatments including pig cadavers (placed on the ground and hung one metre above ground) and two controls (bare soil and bags filled with soil placed on the ground i.e. “fake pig” treatment). In the first two months (15–59 days after the start of the experiment), cadavers caused significant increases of ammonium, nitrogen, phosphorous and potassium (p<0.05) whereas nitrate significantly increased towards the end of the study (263–367 days; p<0.05). Soil pH increased significantly at first and then decreased significantly at the end of the experiment. After one year, some markers returned to basal levels (i.e. not significantly different from control plots), whereas others were still significantly different. Based on these response patterns and in comparison with previous studies, we define three categories of chemical markers that may have the potential to date the time since death: early peak markers (EPM), late peak markers (LPM) and late decrease markers (LDM).The marker categories will enhance our understanding of soil processes and can be highly useful when changes in soil chemistry are related to changes in the composition of soil organism communities. For actual casework further studies and more data are necessary to refine the marker categories along a more precise timeline and to develop a method that can be used in court.

Pedotransfer Functions Bring New Life to Earth System Modeling

A recent paper in Reviews of Geophysics describes how currently available soil information furthers our understanding of soil processes and their integration in Earth system modeling.

Sustainable management of tropical small island ecosystems for the optimization of soil natural capital and ecosystem services: a case of a Caribbean soil ecosystem—Aripo savannas Trinidad

The unsustainable use of soil natural capital and ecosystem services is of global concern due to damage and losses on a worldwide scale. This situation is further compounded in small island developing states (SIDS), such as the Caribbean, where rapid population growth coupled with limited land space accelerates the rate of degradation of soil natural capital. The Aripo savanna is the largest surviving natural savanna in Trinidad with economic and scientific importance. Presently, there are many different land uses and land covers competing for space to the detriment of soil ecosystem services in this savanna. An ecosystem framework approach is needed to guide the development of adaptation strategies to improve the resilience of soil ecosystem for the provisioning of services, especially in the face of climate change. We reviewed the existing literature on soil ecosystem management in SIDS with particular emphasis on Aripo savanna and attempted to provide a better understanding of soil processes by developing frameworks for assessing tropical small island soil ecosystem services and soil health. In tropical island states, poor soil quality has been associated with indiscriminant land use, creating short-term economic viability. Short-term economic viability is characterized by poor practices, negatively impacting on soil and thus limiting its ability to perform ecosystem services. To improve the resilience of a society, an ecosystem-framework approach becomes necessary. Soil ecosystem health, however, cannot be represented solely by specific land use(s)/land cover(s) (LULC) but by critical descriptors that influence soil quality. This review highlights the importance of an ecosystem framework approach for the sustainable management and optimization of soil natural capital and ecosystem services in the Caribbean SIDS.

Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy

AimsStable carbon isotopes are important tracers used to understand ecological food web processes and vegetation shifts over time. However, gaps exist in understanding soil and plant processes that influence δ13C values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δ13C values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δ13C values between five vegetation classes.MethodsThe Land Degradation Surveillance Framework (LDSF) was used to collect a stratified random set of soil samples and to classify vegetation. A total of 154 topsoil and 186 subsoil samples were collected and analyzed using NIRS, organic carbon (OC) and stable carbon isotopes.ResultsForested plots had the most negative average δ13C values, −26.1‰; followed by woodland, −21.9‰; cropland, −19.0‰; shrubland, −16.5‰; and grassland, −13.9‰. Prediction models were developed for δ13C using partial least squares (PLS) regression and random forest (RF) models. Model performance was acceptable and similar with both models. The root mean square error of prediction (RMSEP) values for the three independent validation runs for δ13C using PLS ranged from 1.91 to 2.03 compared to 1.52 to 1.98 using RF.ConclusionsThis model performance indicates that NIR can be used to predict δ13C in soil, which will allow for landscape-scale assessments to better understand carbon dynamics.

Analysis on Fungal Diversity in Rhizosphere Soil of Continuous Cropping Potato Subjected to Different Furrow-Ridge Mulching Managements.

Knowledge about fungi diversity following different planting patterns could improve our understanding of soil processes and thus help us to develop sustainable management strategies. The objective of this study was to determine the impact of different furrow-ridge mulching techniques on fungal diversity in rhizosphere soil under continuous cropping system. The investigated treatments were: flat plot without mulch (CK); flat plot with mulch (T1); on-ridge planting with full mulch (T2); on-furrow planting with full mulch (T3); on-ridge planting with half mulch (T4); and on-furrow planting with half mulch (T5). NGS (Illumina) methods and ITS1 sequences were used in monitoring fungi diversity of the potato rhizosphere soil. The fungi diversity in the rhizosphere soil was ranked in the order T5 > T2 > T4 > T1 > CK at the early growth stage and T2 > T3 > T1 > T4 > CK at the late growth stage of potato. The fungal communities found in the rhizosphere soil were Ascomycota, Zygomycota, Basidiomycota, Chytridiomycota, and other unidentified fungal communities. Among the fungal community in the rhizosphere soil, Ascomycota was found to be dominant fungi population, with the highest percentage (89%) in the T5 soil whereas the T2 soils had the lowest percentage (67%). The Fusarium abundance in fully-mulched treated soils was higher than in half-mulched treated soil. The dominant genus in the T4 soil was Mortierella, whereas lower populations (1–2%) of Scutellinia, Cryphonectria, Acremonium, and Alternaria were found in that treatment. Among the eumycetes, the dominant fungal class in all treated soils was the Sordariomycetes, which ranged from 57 to 85% in T2 and T5 soils, respectively. The Fusarium percentages in half-mulched treated soils (T4 and T5) were 55 and 28% lower than that of complete mulched treated soils (T2 and T3), respectively. The cluster analysis results showed that, CK, T4, and T5 treated soils and T1, T2, and T3 treated soils had similarities in microbial compositions, respectively. Potato tuber yield was greater under the on-ridge planting with full mulch (T2) treated soil, followed by on-ridge planting with half-mulch (T4) treated soil. The rhizosphere soil under the on-ridge planting with full-mulch (T2) soil had the highest fungal diversity, suggesting that this management was the best environment for fungi, whereas the on-ridge planting with half-mulch (T4) soil had the minimum abundance of Fusarium.

Herbicide monitoring in soil, runoff waters and sediments in an olive orchard

Occurrences of surface water contamination by herbicides in areas where olive orchards are established reveal a need to understand soil processes affecting herbicide fate at field scale for this popular Mediterranean crop. A monitoring study with two herbicides (terbuthylazine and oxyfluorfen) in the first 2cm of soil, runoff waters, and sediments, was carried out after under natural rainfall conditions following winter herbicide application. At the end of the 107day field experiment, no residues of the soil applied terbuthylazine were recovered, whereas 42% of the oxyfluorfen applied remained in the top soil. Very low levels of both herbicides were measured in runoff waters; however, concentrations were slightly higher for terbuthylazine (0.53% of applied) than for oxyfluorfen (0.03% of applied), relating to their respective water solubilities. Congruent with soil residue data, 38.15% of the applied oxyfluorfen was found in runoff-sediment, compared to only 0.46% for terbuthylazine. Accordingly, the herbicide soil distribution coefficients measured within runoff field tanks was much greater for oxyfluorfen (Kd=3098) than for terbuthylazine (Kd=1.57). The herbicide oxyfluorfen is co-transported with sediment in runoff, remaining trapped and/or adsorbed to soil particle aggregates, due in part to its low water solubility. In contrast, terbuthylazine soil dissipation may be associated more so with leaching processes, favored by its high water solubility, low sorption, and slow degradation. By comparing these two herbicides, our results reaffirm the importance of herbicide physico-chemical properties in dictating their behavior in soil and also suggest that herbicides with low solubility, as seen in the case oxyfluorfen, remain susceptible to offsite transport associated with sediments.