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Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

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Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

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A traditional grid model for soil sampling may suffer from poor efficiency and low accuracy. With a nonferrous metal processing plant as the study area, a three-dimensional kriging interpolation model was built based on this plant's preliminary investigation data for arsenic (As), and a detailed survey sampling programme was proposed. The sampling density at the pollution interval of the surface soil was estimated by the coefficient of variation method, and the sampling depth was determined by the pollution interval of the vertical prediction results. The results showed that the encrypted soil sampling distribution optimisation method obtains greater pointing accuracy with fewer points. The sampling accuracy was 87.62% after optimising the depth of pointing. Moreover, this approach could save 66.13% of the sampling costs and 56.93% of the testing costs compared to a full deployment programme. This study provides a new and cost-effective method for predicting the extent of contamination exceedance at a site and provides valuable information to guide post-remediation strategies for contaminated sites.

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Methods to measure the diversity and biological control impact of parasitoids for the control of spotted-wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae) are being developed in support of biological control programs around the world. Existing methods to determine parasitism levels and parasitoid species composition focus on sampling D. suzukii within fresh and rotting fruit. However, many D. suzukii pupate in the soil or in dropped fruit, where additional parasitism could occur and where their parasitoids are thought to overwinter. Here we introduce a method for extracting parasitized D. suzukii puparia from the soil through a sieve and flotation system, allowing for effective collection of puparia, from which parasitoids can then be reared. Although the method considerably underestimates the absolute number of puparia in soil samples, it nonetheless yields a high number of puparia relative to sampling effort and provides a robust estimate of the relative abundance of puparia among samples. Using this method, we confirmed that at least 5 species of parasitoids, including some that have rarely been detected in past studies, overwinter in their immature stages inside D. suzukii puparia in south coastal British Columbia, Canada. The ability to sample puparia from the soil will lead to a more comprehensive view of both D. suzukii and parasitoid abundance throughout the season, help confirm parasitoid establishment following intentional releases, and provide a way to measure the diversity of parasitoid species and potential interactions among parasitoids (e.g., hyper- or klepto-parasitism) that may often occur on the soil surface.

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Soil is one type of Earth material demonstrating a wide range of physical, chemical, and biological properties. As the compositional profile of soil is a product of interaction between numerous abiotic and biotic components, it tends to be unique by its geographic origin. Hence, soil is paramount for predicting source or origin in forensic provenance and intelligence, food provenance, biosecurity, and archaeology. In the context of forensic investigation, source tracing of soil could be executed by a comparison or provenance analysis. Soil compositional fingerprints acquired using analytical methods must be carefully interpreted via suitable mathematical and statistical tools since multiple sources can contribute to the variability of soil other than its provenance. This article reviews recent trends in soil sampling and data interpretation strategies proposed for source tracing of soil evidence. Performances of soil provenance indicators are also described. Then, perspectives on possible research directions guiding forensic soil provenance are proposed. This timely critical review reveals the essential idea and gap in forensic soil provenance for stimulating the development of more efficient and effective provenance strategies.

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Here, we introduce a new method for efficiently sampling Chlamydomonas reinhardtii and closely related species using a colony PCR-based screen with novel primer sets designed to specifically detect these important model microalgae. To demonstrate the utility of our new method, we collected 130 soil samples from a wide range of habitats in Ontario, Canada and identified 33 candidate algae, which were barcoded by sequencing a region of the rbcL plastid gene. For select isolates, 18S rRNA gene and YPT4 nuclear markers were also sequenced. Based on phylogenetic and haplotype network analyses of these three loci, seven novel isolates were identified as C. reinhardtii, and one additional isolate appeared to be more closely related to C. reinhardtii than any other known species. All seven new C. reinhardtii strains were interfertile with previously collected C. reinhardtii field isolates, validating the effectiveness of our molecular screen.

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The effects of sampling depth and crop growth stage on the population density of lesion nematodes were investigated in three commercial fields in Wayne and Fulton Counties, Ohio, during the 2016 and 2017 growing seasons. Soil samples were collected at five growth stages by removing 15 soil cores to a depth of 70 cm from each of 25 plots per field-year. Cores were divided into seven 10-cm sections, and nematodes were extracted from the soil and root fractions of each of them. Pratylenchus crenatus and P. thornei were detected in approximately 84 and 78% of the samples collected in Wayne and Fulton Counties, respectively. Depth significantly affected total population density of both species as well as densities in the soil and root factions in all field-years, but the effects of growth stage and its interaction with depth varied with field-year. In most cases, mean population densities were higher from 10 to 40 cm soil depth than at the reference 40 to 50 cm depth and lower from 50 to 70 cm. There were quadratic relationships between population density (on the log link scale) and depth, with the highest peaks in estimated predicted densities generally occurring between 20 and 40 cm, depending on crop growth stage and growing conditions. These findings suggest that the standard practice of sampling between growth stages V3 and V6 to a depth of 45 to 50 cm and using the entire core for extraction and enumeration could lead to underestimation of population densities of P. crenatus and P. thornei.

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Artisanal and small-scale gold mining (ASGM) is the leading global source of mercury pollution. Efforts to reduce or eliminate mercury use in ASGM have produced limited results, in part because they do not engage the complex socio-technical nature of mercury issues in ASGM. The paper takes a multidisciplinary approach to understand the mercury issue with a socio-technical lens, pairing sampling of mercury in soils with surveys of miners' and residents' perceptions of mercury pollution and its dispersion. The research was conducted in Secocha, an ASGM boomtown in southern Peru. Mercury levels in soils exceeded relevant standards in both industrial zones (average of 72.6 mg/kg, versus the Peruvian standard of 24 mg/kg) and residential/urban zones (average of 9.5 mg/kg, versus the Peruvian standard of 6.6 mg/kg). Mercury levels were highest where processing and gold buying activity were concentrated. Surveys revealed that miners and residents correctly assumed mercury pollution to be highest in those areas. However, respondents seemed to underestimate the extent of mercury pollution in other parts of town, and many believed that only those who handle mercury directly were affected by it. Respondents also placed low priority on reducing mercury pollution. Miners' and residents' partial knowledge about mercury contamination and the low priority accorded to the issue suggest that mercury reduction efforts would likely be met with indifference and potentially resistance.

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A comprehensive understanding of tire and road wear particles (TRWPs) and their detection and quantification in soils is still challenged by the lack of well-set standardized methods, inherent technological inconsistencies, and generalized protocols. Our protocol includes soil sampling, size separation, and organic matter removal by using hydrogen peroxide followed by density separation and analysis. In this context, roadside soil samples from different sites in Kansas and Ohio, USA, were collected and analyzed. Tire cryogrinds analogous to TRWPs were used to evaluate various density separation media, and collected particles more than 1 mm in size were then subjected to infrared spectroscopy (IR), thermogravimetric analysis (TGA), and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) to confirm TRWP presence. Particles smaller than 1 mm were Soxhlet extracted, followed by gas chromatography-mass spectrometry (GC-MS) to validate the presence of tire-related intermediates. SEM-EDX validated the presence of elemental combinations (S + Zn/Na) ± (Al, Ca, Mg, K, Si) attributed to tires. Ketones, carboxylic acids, epoxies, cyclohexane, and benzothiazole sulfenamide (BTS) intermediates were the most probable tire-related intermediates observed in the roadside soil samples. Thus, this simple, widely applicable, cost-effective sample preparation protocol for TRWP analysis can assist TRWP research advancement in terrestrial environments.

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This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.

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Developing land suitability models for strategically critical agricultural products to expand sustainable agricultural policies and sensitive agriculture management has become a significant trend. This study aims to improve a unique land suitability model for hazelnut cultivation by applying the criteria set (7 main criteria, 35 sub-criteria) including qualitative and quantitative reasons, integrated fuzzy analytic hierarchy process, inverse distance weighting, multi-criteria decision analysis, geographic information system, and weighted linear combination approaches. The model developed in the present study was applied and tested in Ünye District of Ordu Province, where hazelnut production in the Eastern Black Sea region of Turkey is an important economic activity. While 71.17% of the study area is classified as very highly suitable, highly suitable, and moderately suitable, 28.83% of the study area has marginally suitable and unsuitable properties for hazelnut cultivation. Generally, it was determined that the coastal parts of the study area were the most suitable areas for hazelnut growing. The hazelnut land suitability model's two main criteria impacting the final score values are climatic and topographic conditions, respectively. Heavy metal pollution and physical, chemical, and fertility conditions related to soil properties followed these, respectively. The first ten sub-criteria with the highest weight value were determined as elevation, annual average temperature, annual average precipitation, aspect, annual average relative humidity, nickel (pollution), slope, annual average maximum temperature, lead (pollution), and soil depth, respectively. Existing hazelnut cultivation areas were used to test the model. Of the existing cultivation areas, 75.59% coincided with the very highly suitable, highly suitable, and moderately suitable classes presented in this study, while 17.15% were in marginally suitable and 7.26% in unsuitable classes. The study results reveal that the hazelnut land suitability model developed is suitable in mild climate conditions. Using this model as a general transition model will be beneficial to test it in areas containing similar climatic conditions and various soil properties. This study will create a rational background in ensuring the sustainable food production system and security, agricultural land use planning, strategic planning and management of the hazelnut plant, increasing agricultural productivity and income, and the ecosystem.

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Deterioration of groundwater quality due to nitrate loss from intensive agricultural systems can only be mitigated if methods for in-situ monitoring of nitrate leaching under active farmers' fields are available. In this study, three methods were used in parallel to evaluate their spatial and temporal differences, namely ion-exchange resin-based Self-Integrating Accumulators (SIA), soil coring for extraction of mineral N (Nmin) from 0 to 90 cm in Mid-October (pre-winter) and Mid-February (post-winter), and Suction Cups (SCs) complemented by a HYDRUS 1D model. The monitoring, conducted from 2017 to 2020 in the Gäu Valley in the Swiss Central Plateau, covered four agricultural fields. The crop rotations included grass-clover leys, canola, silage maize and winter cereals. The monthly resolution of SC samples allowed identifying a seasonal pattern, with a nitrate concentration build-up during autumn and peaks in winter, caused by elevated water percolation to deeper soil layers in this period. Using simulated water percolation values, SC concentrations were converted into fluxes. SCs sampled 30% less N-losses on average compared to SIA, which collect also the wide macropore and preferential flows. The difference between Nmin content in autumn and spring was greater than nitrate leaching measured with either SIA or SCs. This observation indicates that autumn Nmin was depleted not only by leaching but also by plant and microbial N uptake and gaseous losses. The positive correlation between autumn Nmin content and leaching fluxes determined by either SCs or SIA suggests autumn Nmin as a useful relative but not absolute indicator for nitrate leaching. In conclusion, all three monitoring techniques are suited to indicate N leaching but represent different transport and cycling processes and vary in spatio-temporal resolution. The choice of monitoring method mainly depends (1) on the project's goals and financial budget and (2) on the soil conditions. Long-term data, and especially the combination of methods, increase process understanding and generate knowledge beyond a pure methodological comparison.

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The Theory of Sampling as developed by Pierre Gy is a complete theory that describes sampling errors and how to obtain a representative sample. Unfortunately Gy's formula for prediction of the Fundamental Sampling Error (FSE) can be difficult to use in practice, as it is only valid for binary materials with same size distribution of analyte containing fragments and matrix fragments. An extended Gy's formula for estimation of FSE is derived from Gy's definition of constitutional heterogeneity. This formula is exact with no assumptions and allows prediction of FSE for any particulate material with any number of particle classes in contrast to Gy's formula. The difference is that the only assumption made is that the sampled material can be divided into classes with similar properties for the fragments within each class. The extended Gy's formula is validated by model experiments sampling mixtures of 3-7 components with a riffle splitter with 18 chutes. In most cases the observed sampling error was well predicted by the newly derived, extended Gy's formula. However, in some experiments the observed sampling errors were lower than FSE. This can be explained by the sampling paradox, and the effect is calculated by a new function, the Fundamental Sampling Uncertainty, FSU. The observed results are typically in excellent agreement with the predictions (the predicted uncertainties were on average 0.5% points lower than the observed values). The extended Gy's formula described here is ideal for use in teaching of sampling methods because the experiments can be set up using materials with accurately known properties. The proposed new formula allows accurate prediction of FSE and FSU for complex materials that contain more than two types of particles.

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RESUMO A representatividade da amostragem é fundamental para embasar a tomada de decisão, uma vez que a heterogeneidade da matriz é o principal fator para incerteza dos dados nos estudos de gerenciamento de áreas contaminadas. Neste estudo, coletaram-se 112 amostras de solo superficial em malha quadrada de 1 x 1 m, em uma área 15 x 6 m, no campus regional de Resende da Universidade do Estado do Rio de Janeiro. Anteriormente, no terreno funcionaram empresas de produção de filmes radiográficos e purificação de prata metálica. As amostras de solo foram analisadas por pXRF, método de screening para metais. Com base nos dados levantados, calculou-se, por meio de métodos estatísticos, a quantidade de amostras que permitiriam uma caracterização representativa da área, considerando um erro relativo de 20% para dois intervalos de confiança: 95 e 80%. As análises indicaram concentrações superiores aos valores de referência para os parâmetros prata, mercúrio, chumbo, selênio e zinco. Os resultados de mercúrio possivelmente referem-se a ruído espectral, não estando relacionados ao histórico de uso e ocupação. Os coeficientes de variação das concentrações dos elementos analisados variaram de 132 a 225%. Considerando uma distribuição lognormal, calcularam-se 76 amostras para o maior intervalo de confiança e 53 para o menor. Isso seria o equivalente a transectos com espaçamentos de 1,47 e 2,11 m. Os resultados ratificam a necessidade de investigações de alta resolução nas áreas fonte, conforme recomendado pela Decisão de Diretoria da Companhia Ambiental do Estado de São Paulo nº 038/2017/C (DD-038).

ABSTRACT The representativeness of the sampling is fundamental to support decisions, since the heterogeneity of the matrix is the main factor for data uncertainty in contaminated areas management studies. In this study, 112 surface soil samples were collected in a square mesh of 1 x 1 m, in an area of 15 x 6 m, at the Campus Regional de Resende (CRR) of Universidade do Estado do Rio de Janeiro (UERJ). Previously, companies of radiographic films production and metallic silver purification operated in this area. The soil samples were analyzed by pXRF, a screening method for metals detection. Based on the data collected, the number of samples that would allow a representative characterization of the area was calculated, using statistical methods, considering a relative error of 20% for 80 and 95% confidence intervals. The analyses indicated concentrations higher than the reference values for the parameters silver, mercury, lead, selenium, and zinc. The results of mercury possibly refer to spectral noise, not being related to the history of use and occupancy. The coefficient of variation of the soil obtained ranged from 132 to 225%. Considering a lognormal distribution, 76 samples were calculated for the highest confidence interval and 53 for the lowest. This would be the equivalent of transects with spacings of approximately 1.47 and 2.11 m. The results confirm the need for high resolution investigations in the source areas, as recommended by the Executive Board Decision of CETESB No. 038/2017/C (DD-038).

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Predictive modeling with remotely sensed data requires an accurate representation of spatial variability by ground truth data. In this study, we assessed the reliability of the size and location of ground truth data in capturing the landscape spatial variability embedded in the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral image in an agricultural region in Anand, India. We derived simulated spectral vegetation and soil indices using Gaussian simulation from AVIRIS-NG image for two point-location datasets, (1) ground truth points from adaptive sampling and (2) points from conditional Latin Hypercube Sampling (cLHS). We compared values of the simulated image indices against the actual image indices (measured) through the analysis of mean absolute errors. Modeling the variogram of the measured indices with the hyperspectral image in high spatial resolution (4m), is an effective way to characterize the spatial heterogeneity at the landscape level. We used geostatistical techniques to analyze the shapes of experimental variograms in order to assess whether or not the ground truth points, when compared against the cLHS-derived points, captured the spatial structures and variability of the studied agricultural area using measured indices. In addition, we explored the capability of the variogram by running tests in different point sample sizes. The ground truth and cLHS datasets were able to derive equivalent values for field spatial variability from image indices, according to our findings. Furthermore, this research presents a methodology for selecting spectral indices and determining the best sample size for efficiently replicating spatial patterns in hyperspectral images.

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The Great East Japan Earthquake caused a serious accident at the first Fukushima nuclear power plant (NPP), which in turn released a large amount of radionuclides. Little attention has been paid to in-situ soil microorganisms exposed to radioactive contamination by the actual NPP accident. We herein investigated bacterial communities in the radioactive cesium (Cs)-contaminated and non-contaminated soils by high-throughput sequencing. The uppermost and ectorhizosphere soil samples were collected from the base of mugwort grown in the same soil type with the same soil-use history in order to compare the bacterial communities at geographically separated areas. The concentrations of radioactive Cs in the soils ranged from 10 to 563,000 Bq 137Cs/kg dry soil, with the highest concentration being detected at 1 km from the NPP. Alpha-diversity indices, i.e., Chao1, Shannon and Simpson reciprocal, of the sequence data showed the lower bacterial diversity in the most highly Cs-contaminated soil. Principal coordinate analysis with principle components 1 and 3 based on unweighted UniFrac distances indicated the significant difference in bacterial communities of the most contaminated area from those of the other areas. Operational taxonomic unit-based assay revealed higher abundance of the radio-resistant Geodermatophilus bullaregiensis relative in the most contaminated soil. Thus, it was strongly suggested that the radioactive accident facilitated the growth and/or survival of radio-resistant bacteria in the Cs-contaminated soils. The results of this study show that information on the soil type, vegetation and soil-use history enhances the direct comparison of geographically distant soil bacterial communities exposed to different levels of radioactive contamination.

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Arbuscular mycorrhizal fungi (AMF) are an important element of the plant microbiome as they establish an endosymbiotic relationship with the roots of most plant species. This association enhances access to nutrients and water for plants, and provides the fungus with plant-derived organic carbon. In this chapter, I describe a range of methods to work with AMF including: soil sampling; isolation of AMF propagules (spores, sporocarps, roots, and mycelium) by a wet sieving and centrifugation in a sucrose solution; trap (from field soil with AMF spores) and one-species pot cultures (from AMF spores divided into morphotypes); staining of mycorrhizae in plant roots; and production of diagnostic slides. These methods are widely used in taxonomic and ecological studies to characterize the morphology of AMF.

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Ailanthus altissima is an invasive alien species (IAS) present throughout Europe and included in the list of alien species of Union concern. In sub-Mediterranean areas of central Italy, there is a lack of knowledge about this invasive species and its interactions with the native forest ecosystems. We aim to find what are the main differences in vegetation structure and floristic diversity between A. altissima forests and native forests through the assessment of the principal ecological parameters that differ between the forest types. We performed 38 phytosociological relevés and sampling of ecological parameters in A. altissima forest communities and neighboring native forests. We analyzed how species richness, diversity, life forms, life strategies, structural characteristics, and ecological parameters changed in A. altissima forests compared with native ones. We found that in A. altissima forests, there is a shift in herbaceous layer richness, with a higher presence of annual ruderal herbs and the absence of herbaceous species linked to the forest environment. The ecological parameters that diverge from the native forests were total nitrogen, total carbon, and C/N ratio. A. altissima forest communities could threaten the biodiversity of the native forest ecosystems in the sub-Mediterranean landscape, favoring ruderal species and inhibiting the presence of typical forest species.

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This study compares four stocking rates (37, 74, 111 and 148 pigs ha-1) for growing to finishing pigs (18.4 ± 0.5 kg and 118.5 ± 2.0 kg and 35.7 ± 2.1 kg and 125.7 ± 2.3 kg initial and final BW for grazing periods 1 and 2, respectively) and their effect on ground cover and soil traits in bermudagrass (Cynodon dactylon [L.] Pers) pastures, over two 14-week grazing periods (July-September and May-August). The study was conducted at the Center for Environmental Farming systems at the Cherry Research Station, Goldsboro North Carolina. A continuous stocking method was implemented to manage the pasture. The percent ground cover was estimated with a modified step point technique. Soil samples were collected in three sampling positions (center, inner and outer areas of the paddocks) and two soil sampling depths (0-30 and 30-90 cm). The experimental design was a completely randomized block with three field replicates. Data were analyzed using the PROC GLIMMIX procedure of SAS/STAT ® Version 9.4. Greater ground cover and lesser soil nutrient concentrations were registered in bermudagrass paddocks managed with 37 pigs ha-1. The results of this study also validated the existence of a spatial pattern of soil properties, which differed among sampling positions and depths.

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The novel approach for optimising soil sampling strategies in areas affected by radionuclides is suggested. Major factors influencing the efficiency of soil sampling strategies, including (number of samples, sampling area size, sampling depth and spatial resolution of the sample sites are examined to provide optimisation of the soil sampling plan. The experimental field studies to validate the suggested approach were performed in 25 sampling units ranging from 1.2 × 1.2 m to 60 × 60 m size. The sampling units were selected on arable farmlands, natural meadow and former agricultural land), as well as coniferous and deciduous forests with contamination density of 137Cs ranging from 2.8 kBq·m-2 to 24.5 MBq·m-2. The studied areas were contaminated by both the global fallout and the Chernobyl radioactive particles of different types. To determine the values of standard deviation of the log of the soil contamination density of 137Cs, 25 to 256 soil samples were collected with an increment of 0.07-10 m within each sampling unit. It was found that the values of standard deviation of the log of the soil contamination density of 137Cs were not dependent on the mean contamination density, the type of radioactive deposition and the landscape features. The mean value of standard deviation calculated for all sites studied was estimated as 0.44 ± 0.15 and 0.30 ± 0.10 for the sampling area 0.001 m2 (∅37 mm) and 0.005 m2 (∅80 mm) at the relative measurement uncertainties lower than 10% (CI = 95%). Concentrations of 137Cs in the soil samples were statistically independent when sampling points were situated at a distance larger than 1 m one from each other. A simple method was developed for assessing minimum sample sizes required for estimation of the median or the geometric mean of radionuclide soil contamination with a relative uncertainty set by the user. The approach was also suggested for estimation of the uncertainty of soil contamination for the case of composite samples. The approach was implemented in the Ukrainian national requirements for assessment of quality of the soil.

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