Long-term Soil Monitoring Research Articles

Sustainable farming practices enhance bacterial diversity and nutrient levels in sorghum rhizosphere soil

This research employs metagenomic analysis to explore bacterial diversity in sorghum rhizosphere soil in response to various sustainable farming practices and their impact on soil fertility. The field experiment with cotton-sorghum cropping system was conducted since 2020 and from 3rd cycle sorghum experiment, the soils were collected and subjected to metagenomics and nutrients availability analysis. We evaluated the impact of different farming approaches: Natural Farming (NF), Organic Farming (OF), integrated crop management (ICM), along with a control (No input farming). The results underscores the prevalence of Proteobacteria phylum across all farming practices, while Actinobacteria, Bacteroidetes and Firmicutes showed increased presence due to altered soil management and nutrient availability changes. Genus-level analysis revealed shifts in dominant genera, including Acinetobacter, Sphingomonas, Bacillus, and Candidatus nitrososphaera, across different farming practices. Post-harvest soil analysis indicated varied nutrient levels, with ICM showing enhanced nitrogen, phosphorus, and potassium availability. ICM, utilizing a balanced mix of organic and inorganic fertilizers, promoted sorghum growth, yielding higher plant height and total dry matter compared to OF and NF, which performed similarly. The study emphasizes the efficacy of a balanced approach like ICM for improving crop production and soil nutrient availability. However, all diversified farming practices exhibited high soil biological diversity compared to the control, highlighting the necessity for continuous long-term soil monitoring across different farming practices to stabilize soil microbiomes, ensure nutrient availability, and support sustainable crop production.

2016–2021年上海长三角站长期监测样地土壤基本性状数据集

Shanghai Yangtze River Delta Ecology & Environmental Change and Control Research Station is one of the four national field scientific observation and research stations for urban ecosystems approved in 2021. Since 2016, the Station has carried out long-term soil monitoring. This dataset collects the basic soil properties of 12 long-term monitoring plots in different soil layers (0-10 cm, 10-20 cm, 20-40 cm) on different urban-rural gradients at the Station from 2016 to 2021, including pH value, bulk density, total carbon, total nitrogen, total phosphorus, total potassium, available phosphorus, available potassium, total zinc, total copper, total lead and total chromium. Moreover, the dataset includes supplementary information such as plot details, soil sampling and preservation procedures, and a table outlining the analysis methods employed. The dataset can be provided as data basis for the study of soil carbon sinks, soil nutrient changes, and heavy metal pollution in megacities.

Microplastic in long-term soil monitoring: first spatial and temporal data on plastics in agricultural topsoils

PurposeWithin the emerging field of soil related microplastic (MP) research, little is known about spatio-temporal variations of MP concentrations in soils. To overcome this current knowledge gap, we suggest the exploitation of long-term soil monitoring programmes (LTSM) and its archived soil samples, to gain first data on spatio-temporal variations within soil MP contamination.MethodsWe analysed 32 archived topsoil samples of 13 cropland and 3 control sites, including grassland and forest sites, which are part of the LTSM programme of the German federal state of Hesse. Analysed samples cover a time span of 10–16 years. MP (>300 µm) have been extracted via sodium chloride (NaCl, 1.2 g cm−3) density separation, Nile Red staining and fluorescent optical identification followed by ATR-FTIR analysis of individual MP.ResultsWe found MP contamination (mean 34.66 ± 24.08 p kg−1) from mainly PE and synthetic rubber polymers. Furthermore, we were able to detect spatial MP contamination hotspots, with unexpected high MP concentrations in control grassland and forest LTSM sites. In case of temporal trends, we cannot conclude a general increase of MP concentrations over time. However, as we found MP in older and newer samples, MP contamination is likely to be ubiquitous in topsoils and has already been present in the environment for several decades.ConclusionOur findings underline the role of cropland topsoils as MP reservoirs, but raise questions on the MP storage in grassland and forest soils. Applying MP analytical tools to archived LTSM samples enables first insights within non-targeted spatial source analysis and temporal trends of soils MP pollution. If enhanced analytical methods will be applied to LTSM samples, it will be possible to gain more detailed insights within spatio-temporal MP dynamics in soils.

Pesticide residues in agricultural soils in light of their on-farm application history

The application of synthetic pesticides to agricultural fields for the protection of crops leads to the formation of residues in soils. While the short-term behavior of pesticide residues in soils after an application is generally known from laboratory and field studies required for authorization (prospective risk assessments), there is still a lack of in-situ observations that address their long-term fate. Long-term soil monitoring programs, with comprehensive site-specific records of pesticide application data, constitute an invaluable, complementary, retrospective exposure assessment tool to address this gap. Considering the pesticide applications over the past 10–15 years, this study assessed the occurrence of pesticides in agricultural soils of Switzerland and put their presence or absence, as well as their concentrations, in the context of their previous application. The results showed that pesticides could also be detected at sites without a connection to previous applications and that small residual mass fractions of pesticides, even of some non-persistent compounds, were found in soils, years or decades after their last application. This finding points to an environmental issue that may not be adequately captured in prospective risk assessment and calls attention to the need for comprehensive long-term recording and monitoring as a complementary retrospective exposure assessment.

Priorities for management and protection of Québec soils

• Québec continental land cover is large and occupied by agriculture, forests, wetlands, water bodies, and tundra. • Agricultural land base is small and soils need to be managed for both sustained food production and environmental protection • Huge soil carbon stocks in forest and peatlands, including permafrost peatlands, need to be preserved to avoid future emissions • Long-term soil monitoring in all ecosystems is crucial in order to adapt measures of conservation and sustainability • There is a need to increase awareness to soil related issues among the various stakeholders and the general public

Data accuracy and method validation of chemical soil properties in long-term experiments: Standard operating procedures for a non-certified soil laboratory in Latin America

Long-term agricultural experiments (LTE) are essential to detect cumulative treatment effects on soil properties and to design sustainable production systems. However, to ensure high quality of long-term soil data and their correct interpretation, several analytical issues regarding the accuracy and analytical laboratory bias need to be considered. This paper aims to (1) evaluate laboratory precision and trueness of analytical soil data for the evaluation of long-term trends in LTE and to (2) assess the limitations and challenges for non-certified soil laboratories that might compromise the quality of analytical soil data. A data set of internal reference soil materials (IRM) collected over 16 years and interlaboratory data from eleven years were analyzed to verify method precision, trueness, and the subsequent long-term dataset reliability for several soil quality parameters: organic carbon (SOC, determined either by wet or dry combustion), pH (water), extractable phosphorous (either Bray I or citric acid; Bray-P or citric acid-P), and exchangeable potassium (Kexch). Results showed that IRM used by the laboratory were homogenous in terms of physical and chemical composition and appropriate to confirm the precision of long-term soil survey data. The relative standard deviation for repeatability and reproducibility (RSDR) ranged from 1.5% for SOC by wet combustion to 9.5% for citric acid-P. HorRat values (the ratio of the estimated standard deviations of reproducibility and the repeatability found for individual analytical procedures) for all chemical soil properties were within the acceptable ranges of <2.0. Interlaboratory trials for soil pH and SOC showed tolerable standard Z-Scores under 2.0 (Z-Score, calculated from the difference between laboratory results and the assigned value divided by the standard deviation), verifying the trueness of data. The results of this study reinforce the validity of analytical soil data originating from the non-certified laboratory in Uruguay obtaining both precise and true soil quality data over a long period of time for most soil analytes. Nevertheless, the analytical flaws in LTE soil monitoring were revealed and can only be minimized in ongoing and future studies through the inclusion of certified reference material. These recommendations should guide future research activities in LTE studies on analytical data quality management as a requirement for long-term soil monitoring. Finally, the paper proposes a proficiency testing procedure for soil laboratories to achieve and maintain high analytical quality for LTE soil research.

Site and land-use associations of soil bacteria and fungi define core and indicative taxa.

ABSTRACTSoil microbial diversity has major influences on ecosystem functions and services. However, due to its complexity and uneven distribution of abundant and rare taxa, quantification of soil microbial diversity remains challenging and thereby impeding its integration into long-term monitoring programs. Using metabarcoding, we analyzed soil bacterial and fungal communities at 30 long-term soil monitoring sites from the three land-use types arable land, permanent grassland, and forest with a yearly sampling between snowmelt and first fertilization over five years. Unlike soil microbial biomass and alpha-diversity, microbial community compositions and structures were site- and land-use-specific with CAP reclassification success rates of 100%. The temporally stable site core communities included 38.5% of bacterial and 33.1% of fungal OTUs covering 95.9% and 93.2% of relative abundances. We characterized bacterial and fungal core communities and their land-use associations at the family-level. In general, fungal families revealed stronger land-use associations as compared to bacteria. This is likely due to a stronger vegetation effect on fungal core taxa, while bacterial core taxa were stronger related to soil properties. The assessment of core communities can be used to form cultivation-independent reference lists of microbial taxa, which may facilitate the development of microbial indicators for soil quality and the use of soil microbiota for long-term soil biomonitoring.

Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2&gt;0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

Expatiating the impact of anthropogenic aspects and climatic factors on long-term soil monitoring and management.

This article is an extensive collection of scientific literature related to the impact of fertilizers on soil microbial and enzymatic activity. Due to the significance of technology in quantitative and qualitative evaluation of agricultural production, this is a basic problem for the present and future of mankind, where the scientific data being of utmost importance related to the topic. The comparison, including pedo-enzymological evaluation of minerals along with organic fertilization, highlights significant differences between mineral and organic fertilizers, confirming the superiority of complex mineral-organic fertilization. Enzymatic indicators that describe and define the soil quality resulted from enzymatic activities value and provide valuable information regarding the soil fertility status. Moreover, soil enzyme responds to soil management as well as to environmental pollutants. Changes of environmental conditions and pollutants like heavy metals and other toxic substances result in a shift in the biological activity of the soil. These changes can destabilize the soil system and cause a decrease in the nutrient pools. To ensure the improvement of fertilization techniques, the properties of nanoparticles are exploited that can efficiently release nutrients to plant cells. Numerous researches were performed in order to follow the long-term effects of incorporating nanofertilizers into the soil, obtaining an exhaustive overview of this new technology over the development of sustainable agriculture.

Intensive long-term monitoring of soil organic carbon and nutrients in Northern Germany

Since 2003, the regional long-term soil monitoring network (SMN) Schleswig–Holstein (SH) includes an intensive monitoring program (I-BDF) with (sub-)annual measurements at four sites. This is the first study investigating the benefits of this SMN where study sites are no experiments but managed by independent farmers. The main objective of this study was to investigate whether, and under which circumstances, annual soil carbon and nutrient measurements are more beneficial within a soil monitoring network than common five- to ten-year measurements using modeling and nutrient balances. Soil measurements (stocks of soil organic carbon (SOC), Ntot, P and Mg), weekly leachate-NO3–N and management data were used for comparison. C and N changes were modeled with DNDC (DeNitrification–DeComposition); P and Mg were calculated as full nutrient balances and compared to the observations using performance metrics. The results show that DNDC could reproduce the long-term trend of SOC and Ntot well, but this could also be by coincidence as the type of trendline depended on the starting year. The model results could not depict measured short-term variations in soil which were due to field heterogeneities caused by farm management. NO3-N leaching was strongly overestimated when organic fertilization and stronger rainfall occurred. Comparing stock changes with nutrient balances revealed that, in several cases, long-term trends could be shown to a limited extent and reproduced only very few short-term changes and variations. The results suggest that only annual soil property measurements can depict the soil’s variability and contribute to the identification of the true long-term trend.

Developing characterization factors to quantify management impacts on soil quality of paddy fields within life cycle assessment

Abstract Soil quality varies greatly with the management of agricultural land such as the application of fertilizer. Quantifying the impact on soil quality at a management level has been recognized as an urgent issue in life cycle assessment (LCA). This study is the first to estimate characterization factors for land occupation under different soil type, fertilizer type, and application period, which is defined as 1 (the optimum quality of a soil) minus the soil quality index (SQI). The SQI for paddy field of Japan are estimated as follows. First, SQI at each site per year was calculated by considering five soil functions and 17 soil properties collected from a long-term soil monitoring database. Then, time-dependent SQIs over 20 years were then estimated using a statistical model for different combinations of three soil types and five fertilizer types (chemical fertilizers, chemical fertilizers with rice straw, chemical fertilizers with rice straw compost, rice straw, and rice straw compost). The results showed that the SQI is influenced by both soil type and fertilizer type. For fertilizer type, continuous use of chemical fertilizers lowered the SQI. The addition of rice straw to chemical fertilizers mitigated the decrease of the SQI, but the addition of more compost increased the SQI. Longer application periods led to larger differences between the SQI of each fertilizer type, which suggests that it is important to calculate time-dependent characterization factors. The introduction of soil quality impact assessment based on the new characterization factors into an LCA of agricultural system is expected to improve to capture the difference among managements of agricultural land.

Farmers as data sources: Cooperative framework for mapping soil properties for permanent crops in South Tyrol (Northern Italy)

Detailed knowledge of agricultural soil properties is a key element for high-quality food production. However, high-resolution soil data covering a large agricultural region are generally unavailable. This study explores a demand-driven cooperative framework for soil data sourcing that connects individual farmers to several stakeholders by means of a centralised database containing more than 16,000 records of soil information collected within the framework of an integrated production program for intensively managed permanent crops in the Adige/Etsch and Venosta/Vinschgau valleys in South Tyrol, Italy. Data for soil pH, soil organic matter (SOM), and soil texture were used to produce digital soil maps with a RMSE of 0.21, 1.25% and a cross-validation of 43%, respectively. Spatialisation was conducted using either regression-kriging or multinomial logistic regression. Collaboration among farmers, public administrators, and researchers provided a successful cooperative framework for digital soil mapping. The maps highlight the complex interplay of the postglacial evolution of these valleys due to the presence of a cluster of large alluvial fans and the anthropogenic influences of intense farming on pH, SOM, and soil texture. This study regarded a subset of the available soil properties, which can be dealt with using the geostatistical approaches presented herein. Thus, a long-term soil monitoring program and the combination of all available variables will allow digital assessment of the spatial patterns of nutrient availability, ecological risk assessments, change detection studies, and an overall long-term plan for soil security at larger spatial scales.

Quantifying biogeochemical heterogeneity in soil systems

Soils are increasingly perceived as complex systems with properties and biogeochemical functions that vary on millimeter scales. Quantitative information about the resulting biogeochemical heterogeneity is needed to improve process knowledge and to render biogeochemical models more mechanistic. Here we demonstrate how standardized arrays of Pt-electrodes can be used to quantify biogeochemical or ‘functional’ soil heterogeneity, defined as the extent to which the soil is subdivided into microenvironments. Our case study confirmed the validity of this approach for a soil sequence consisting of a well-drained, a moderately well drained and a poorly drained Mollisol. We found that (i) variations in soil moisture content are the immediate cause for variations in functional heterogeneity, with (ii) soil porosity influencing rates and the magnitude of change. We posit that the deployment of standardized arrays of Pt-electrodes will offer an affordable option to monitor the general metabolic state of the soil system and simultaneously quantify the functional heterogeneity of underlying processes at any point in time. Such information should be useful to improve quantitative estimates of processes as diverse as trace gas emissions, trace gas consumption, reductive dehalogenation and mobilization of metals in the subsurface biosphere.We recommend that parameterization of functional soil heterogeneity be included in long-term soil monitoring programs such as Superfund Sites, Critical Zone Observatories, Long Term Ecological Research (LTER) and National Ecological Observatory Network (NEON) sites.

Long-term monitoring of soil quality changes in Northern Germany

The German regional long-term soil monitoring network (SMN) BDF-SH (Boden-Dauerbeobachtung Schleswig-Holstein) was assessed focusing on the quality of this soil monitoring network to detect and evaluate impacts of land-use change (LUC) from pasture to arable land on soil organic carbon (SOC) and biological soil properties. This included a review and evaluation of the monitoring methods to detect long-term SOC changes over time. Two loamy LUC study sites were selected from the monitoring program. The BDF-SH is not only focusing on monitoring of soil organic carbon but also on a wide range of data of soil chemistry, physics, biology and management at field scale. The quality of the SMN as a SOC monitoring was assessed using a catalogue of essential soil monitoring requirements which resulted in a classification of monitoring levels for each parameter. A SMN-specific method is given how to calculate SOC stock changes over time. Within seven and one year(s) respectively the conversion from pasture to arable land resulted in significant SOC losses of 19.4Mgha−1 (19.8%) at the sandy loam site 11 and 27.2Mgha−1 (20.2%) at the clay loam site 13. SOC measurements and microbiological parameters of the soil microbiological program confirmed and defined the results of the main program more precisely. Soil faunistic results underlined the impact of LUC on the soil ecosystem. Using evaluation schemes of the literature the quality of the SMN was evaluated as highly suitable to detect and evaluate SOC stock changes over time and further LUC impacts on the soil. Most of the assessed monitoring parameters of the SMN were evaluated as fulfilling the highest level. However, a SOC fractionation method could be included in the SMN to enable an even more thorough evaluation of SOC stock changes using SOC fraction measurements in process-based SOC modeling.

Soil quality assessment for peat-mineral mix cover soil used in oil sands reclamation.

A soil quality (SQ) assessment and rating framework that is quantitative, iterative, and adaptable, with justifiable weighting for quality scores, is required for evaluating site-specific SQ at land reclamation sites. Such a framework needs to identify the minimum dataset that reflects the current knowledge regarding relationships between SQ indicators and relevant measures of ecosystem performance. Our objective was to develop nonlinear scoring functions for assessing the impact on SQ of peat-mineral mix (PMM) used as a cover soil at land reclamation sites. Soil functional indicators affected by PMM were extracted from existing databases and correlated with soil organic carbon (SOC). Based on defined objectives for SQ assessment, indicators with significant correlation ( < 0.05) to SOC were selected, normalized, and fitted to sigmoid functions using nonlinear regression procedure to establish SQ functions (SQFs) that can analyze changes in field capacity, permanent wilting point, soil nitrogen, and cation exchange capacity of PMM using SOC as input parameter. Application of the SQFs to an independent dataset produced ratings with mean differences similar to the treatment effects of mixing three levels of peat and mineral soil. These results show that derived ratings and weighing factors using SOC reflect the relationship between PMM treatment and other SQ indicators. Applying the developed SQFs to a long-term soil monitoring dataset shows that an increase or decrease in SOC from 10 to 20 g kg causes a significant change in SQ. This identifies the need for further nutrient and moisture management of PMM to support long-term SQ development in land reclamation.

Traceabilty of environmental soil measurements

Traceability is a key concept in environmental soil measurements, and should be linked with any soil measurement, especially long-term soil monitoring, which needs to demonstrate measurement quality. This article presents the main soil measurements of environmental concern, including traceability, uncertainties and possible errors, for each step in the process. It starts with sampling the soil in the field, treatment and conditioning in the field, transportation, storage and preservation of the sample in the laboratory, then goes on to the pre-analytical treatment and preparation for analyses, including the main extraction methods and analytical techniques and instruments employed. The article highlights the traceability links that should be considered for soil measurements to achieve data comparability.

Soil monitoring in Germany

Background Aim and Scope Soil monitoring in Germany should register the current soil condition, monitor its changes and provide a forecast for future development. In order to achieve these goals, the long-term soil monitoring sites in Germany (BDF -Bodendauerbeobachtungsflachen) have been established by the federal states. This has been done according to criteria worked out by soil monitoring experts. In this article a method for the examination of the suitability of Germany’s soil monitoring sites for soil conservation and protection purposes, as well as for environmental monitoring and reporting, is introduced. This method includes the landscape representativity of soil monitoring sites as well as the comparability and spatial validity of collected data.

Spatial Variation of Urease Activity Measured in Soil Monitoring

Since time and money are usually limited, researchers need to optimize and select sampling scales that reflect the spatial variation of the properties under consideration. This paper addresses the question how sampling designs can be evaluated with respect to the selection of sampling scales when monitoring soil enzyme activity. The proposed methodology is illustrated by studying the spatial variation of urease activity and organic C content at three sites that have different types of land use (pasture, arable land, and forest) as an example. At each site, an area of 0.75 hectares was sampled using a hierarchical multistage sampling scheme called nested sampling. Large differences in both the statistical and spatial distributions were observed between the three sites. For the arable land, a considerable part of the total variance of the two variables, urease activity and organic C content, could be statistically explained by stratifying the samples according to soil color, thereby reflecting differences in the origin of the organic matter. No correlation between the two variables was found within the forest and the pasture site, as well as within each of the two strata distinguished by soil color on the arable land site. Spatial autocorrelation of urease activity was found only for sample spacings of <1 m for the pasture site, while autocorrelation extended up to 15 m for the other two sites. To represent the full site-specific range of spatial variation, the sample spacings must encompass these distances. Because of its efficiency in identifying spatial scales of variation, nested sampling is especially well suited for application to pilot surveys by providing a basis for the design of more intensive sampling campaigns, including long-term soil monitoring programs.