Field Scale Research Articles

The impact of bedding planes on microwave-induced damage in rock at the field scale: a numerical model

The impact of bedding planes on microwave-induced damage in rock at the field scale: a numerical model

Organic farming and semi‐natural habitats for multifunctional agriculture: A case study in hedgerow landscapes of Brittany

Abstract Finding more sustainable ways to produce food is a major challenge for humanity in the face of biodiversity extinction and climate change. Consequently, research on the ability of agroecosystems to provide multiple functions is growing. In this regard, the relative importance of organic farming and landscape‐scale measures for improving multifunctionality has recently been debated. We investigated the effects of farming system (conventional vs. organic) at field scale, total length of hedgerows in the landscape and their interaction on the multifunctionality of 40 winter cereal fields in Brittany (France). Our multifunctionality assessment integrated 21 indicators of five agroecosystem goods: biodiversity conservation, nutrient cycling and soil structure, pest and disease regulation, food production and socio‐economic performance. Many indicators of biodiversity conservation, pest and disease regulation, and socio‐economic performance were higher in organic than in conventional systems. However, indicators of nutrient cycling and soil structure did not improve and food production was much lower in organic systems. Total hedgerow length in the landscape had less influence than organic farming on indicators, although we observed positive interactions. Granivorous carabid abundance and semi‐net margin were highest in organic fields located in well‐preserved hedgerow landscapes. Synthesis and applications. Our study suggests that field‐scale organic farming is necessary to promote biodiversity conservation and associated ecological functioning in crop fields, whereas landscape‐scale preservation of semi‐natural habitats alone is likely insufficient. Preservation of hedgerows in the landscape brings additional ecological and socio‐economic benefits for organic systems without compromising agricultural production. More broadly, our results call for more ambitious research into the myriad possible combinations of farming practices and agri‐environmental measures at both field and landscape scales, to improve both below‐ground and above‐ground functioning.

Field‐based estimation of cation exchange capacity using induced polarization methods

AbstractThis study investigates the potential of field‐based induced polarization (IP) methods to provide in‐situ estimates of soil cation exchange capacity (CEC). CEC influences the fate of nutrients and pollutants in the subsurface. However, estimates of CEC require sampling and laboratory analysis, which can be costly, especially at large scales. Induced polarization (IP) methods offer an alternative approach for CEC estimation. The sensitivity of IP measurements to the surface properties of geological materials ought to make them more appropriate than DC resistivity and electromagnetic induction methods, that are sensitive to bulk electrical properties . Such abilities of IP are well demonstrated in the laboratory; however, applications are lacking at field scales. In this work, the ability of field‐based IP to characterize the CEC of floodplain soils is assessed by implementing a methodology that allows for direct comparison between IP and soil parameters. In one field, soil polarization and CEC exhibited the expected positive correlation; but multi‐frequency measurements showed no clear advantage over single‐frequency measurements. In another field, coarser soils (with low CEC) exhibited a high polarization. These coarser soils were characterized by anomalous magnetic susceptibility values, and hence the polarization was attributed to the presence of magnetic minerals. Although better than order‐of‐magnitude estimates of CEC were possible in soils without substantial magnetic minerals, better characterization of porosity, saturation, cementation and saturation exponents, and pore fluid conductivity would improve predictions. However, the measurement of these parameters would require similar efforts as direct CEC measurements. This study contributes to bridging the gap between laboratory‐derived relationships and their applicability in field applications. Overall, this work provides valuable insight for future studies seeking to understand polarization mechanisms in soils at the field scale.

The LTAR Cropland Common Experiment at R. J. Cook Agronomy Farm.

Dryland agriculture in the Inland Pacific Northwest is challenged in part by rising input costs for seed, fertilizer, and agrichemicals; threats to water quality and soil health, including soil erosion, organic matter decline, acidification, compaction, and nutrient imbalances; lack of cropping system diversity; herbicide resistance; and air quality concerns from atmospheric emissions of particulate matter and greenhouse gases. Technological advances such as rapid data acquisition, artificial intelligence, cloud computing, and robotics have helped fuel innovation and discovery but have also further complicated agricultural decision-making and research. Meeting these challenges has promoted interest in (1) supporting long-term research that enables assessment of ecosystem service trade-offs and advances sustainable and regenerative approaches to agriculture, and (2) developing coproduction research approaches that actively engage decision-makers and accelerate innovation. The R. J. Cook Agronomy Farm (CAF) Long-Term Agroecosystem Research (LTAR) site established a cropping systems experiment in 2017 that contrasts prevailing (PRV) and alternative (ALT) practices at field scales over a proposed 30-year time frame. The experimental site is on the Washington State University CAF near Pullman, WA. Cropping practices include a wheat-based cropping system with wheat (Triticum aestivum L.), canola (Brassica napus, variety napus), chickpea (Cicer arietinum), and winter pea (Pisum sativum), with winter wheat produced every third year under the ALT practices of continuous no-tillage and precision applied N, compared to the PRV practice of reduced tillage (RT) and uniformly applied agrichemicals. Biophysical measurements are made at georeferenced locations that capture field-scale spatial variability at temporal intervals that follow approved methods for each agronomic and environmental metric. Research to date is assessing spatial and temporal variations in cropping system performance (e.g., crop yield, soil health, and water and air quality) for ALT versus PRV and associated tradeoffs. Future research will explore a coproduction approach with the intent of advancing discovery, innovation, and impact through collaborative stakeholder-researcher partnerships that direct and implement research priorities.

SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation

Human pose estimation identifies and classifies key joints of the human body in images or videos. Existing pose estimation methods can precisely capture human movements in real time but require significant computational time and resources, which restricts their usage in specific conditions. Thus, we propose a lightweight pose estimation model—SMS-Net—based on the sequentially stacked structure of the hourglass network. The proposed model uses various lightweight techniques to enable high-speed pose estimation while requiring minimal storage space and computation. Specifically, a shuffle-gated block was introduced to reduce the computational load and number of parameters during the feature extraction process of the encoder composing each hourglass network. A multi-dilation block was used in the decoder to secure the receptive fields of various scales without increasing the computational load. The performance of the proposed model was assessed using the MPII and Common Objects in Context (COCO) datasets used for pose estimation and certain performance metrics and compared with state-of-the-art lightweight pose estimation models. Furthermore, an ablation study was performed to assess the impact of each module on network performance and efficiency. The results demonstrate that the proposed model achieved an improved balance between computational efficiency and performance compared to existing models in human pose estimation. Overall, the study findings can provide a basis for applications in computer vision technology.

Experimental and Numerical Study on Nonlinear Shear Behavior and Constitutive Model of Deep Shale Laminae Planes

The direct shear tests showed that the degradation of unevenness and waviness of the laminae plane is the primary reason for the dynamic decrease in shear strength. A shear constitutive model was proposed which considers the scale effect and the asperity geometry of the unevenness and waviness of the laminar plane. The evolution of the shear strength and stiffness with a normal stress and scale effect during the shearing of shale laminae planes was explored. The results show that high normal stress aggravates the stiffness hardening of laminae planes and forms larger peak shear stress and peak shear displacement. At the lab scale, the increase in the unevenness wavelength has a hardening effect on the shear stiffness and strength. The small-scale unevenness contributes most to the shear strength of shale laminae planes at the lab scale. At the field scale, the increase in the waviness wavelength has a softening effect on the shear stiffness and strength.

Upscaling and downscaling approaches for early season rice yield prediction using Sentinel-2 and machine learning for precision nitrogen fertilisation

Early season yield prediction could support rice farmers in adopting precision agriculture for nitrogen fertilisation management. Remote sensing and machine learning (ML) can be used to predict and map crop yield during phenological stages relevant to nitrogen application, like tillering in rice, at both within-field and field scales. This study evaluated the transferability of ML models in early season yield prediction through upscaling and downscaling approaches. The effects of two prediction times (tillering and ripening stages) and training/testing set sizes on ML models performance were evaluated over five rice growing seasons (from 2018 to 2022) in northern Italy, using whole-field-average yields and yield maps. Vegetation indices from Sentinel-2 imagery using the Google Earth Engine platform fed five ML algorithms (Cubist-CUB, Gaussian Process Regression-GPR, Neural Network-NNET, Random Forest-RF, and Support Vector Machines-SVM). ML algorithms were trained with yield maps and tested with whole-field-average yields to obtain a downscaling approach, while the opposite was done to obtain an upscaling approach. The downscaling approach showed higher accuracy than upscaling approach. Ripening stage predictions were more accurate than tillering stages, although the downscaling approach showed small differences between tillering and ripening stages. The highest tillering stage accuracy was achieved by SVM for both downscaling and upscaling approaches with 20 % and 27.8 % of Normalized Root Mean Square Error (NRMSE), and 0.99 and 0.99 of Simple Additive Weighting (SAW) score, respectively. Set size and data distribution effected ML models accuracy, with the highest performance achieved by RF and GPR with 0.80 and 1.00 of SAW score for the downscaling and upscaling approaches, respectively. This study demonstrated how ML models and downscaling approach could support rice farmers to calculate the nitrogen dose using the predicted yield at the tillering stage, enabling them to apply a site-specific nitrogen fertilisation based on the within-field yield prediction variability.

Biofilm growth Dynamics in a Micro-Irrigation with Reclaimed Wastewater in the Field scale.

The dripper clogging due to the development of biofilm can reduce the benefits of micro-irrigation technology implementation using reclaimed wastewater. The narrow cross-section and labyrinth geometry of the dripper channel enhance the fouling mechanisms. The aim of this study was to evaluate the water distribution and biofouling of drip irrigation systems at the field scale during irrigation with treated wastewater. Six 100 m lines of commercial pipes with two pressure-compensating dripper types (flow rate, Q, of 0.65 L.h-1 and 1.5 L.h-1, respectively) were monitored for four months. Different zones along the pipes were selected to evaluate the influence of hydrodynamical conditions (Reynolds number = 5400 to 0) on biofouling. Destructive methods involving the biofilm extraction by mechanical means, showed little biofilm development without significant differences in dry and organic matter content in function of the sampling location along the pipe or dripper flow rate (Q0.65 and Q1.5). These results were confirmed by non-destructive methods, such as optical coherence tomography, that nevertheless showed that biofouling concerned 15-20% of the total dripper labyrinth volume. Total organic carbon monitoring and its composition (by three-dimensional excitation and emission matrix fluorescence microscopy) showed that the biofilm did not significantly influence the organic matter nature. Our results indicated that the biological activity and biofilm development in irrigation systems were more affected by the environmental conditions, particularly water temperature, rather than flow conditions. This confirmed that treated wastewater with low organic content can be used in micro-irrigation systems without significant loss of efficiency, even in conditions requiring intensive irrigation, such as the Mediterranean climate.

Examination of the Dry Density Importances in TDR Use and of the ASTM D6780 Standard Based on a New Calibration Procedure

Abstract The use of time domain reflectometry (TDR) to monitor soil water content at either field or laboratory scales is highly common. Regarding the soil dry density, ρd, effect on the TDR application, three main approaches are found in the literature: (1) Ignore the density issue and use calibration to evaluate the water content regardless of the density. This approach is basic, yet common. (2) Include soil density in the calibration equation and use calibration for the water content as a function of both the TDR output and soil density. In this approach, the soil density should be provided from an independent source. (3) Determined ρd from the TDR waveform. In this case both water content and ρd are set from the TDR analysis. In cases where the ρd is not available, in order to use TDR for water content measurement, a decision has to be made whether to address the density issue although it requires resources, or to neglect it. This article reviews this issue and validates the density effect using an efficient TDR calibration methodology. A calibration scheme that controls ρd of the specimen has been developed and applied. The methodology creates continuous calibration curves over a range of volumetric water content from a single test specimen. Based on the results, it has been demonstrated that the soil density has a major effect on the TDR output and it cannot be ignored. In addition, this article presents a unique examination of ASTM D6780, Standard Test Methods for Water Content and Density of Soil In Situ by Time Domain Reflectometry (TDR). The ASTM dry density estimation was found to provide reliable results; however, the gravimetric water contents estimation gives unsatisfactory results, especially for low densities.

Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland

AbstractWe performed a series of hydraulic stimulations at 1.1 km depth in the Bedretto underground laboratory, Switzerland, as part of an overall research strategy attempting to understand induced seismicity on different scales. Using an ultra‐high frequency seismic network we detect seismic events as small as Mw < −4, revealing intricate details of a complex fracture network extending over 100 m from the injection sites. Here, we outline the experimental approach and present seismic catalogs as well as a comparative analysis of event number per injection, magnitudes, b‐values, seismogenic index and reactivation pressures. In our first‐order seismicity analysis, we could make the following observations: The rock volume impacted by the stimulations in different intervals differs significantly with a lateral extent from a few meters to more than 150 m. In most intervals multiple fractures were reactivated. The seismicity typically propagates upwards toward shallower depth on parallel oriented planes that are consistent with the stress field and seem to a large extent associated with preexisting open fractures. This experiment confirms the diversity in seismic behavior independent from the injection protocol. The overall seismicity patterns demonstrate that multi‐stage stimulations using zonal isolation allow developing an extended fracture network in a 3D rock volume, which is necessary for enhanced geothermal systems. Our stimulations covering two orders of magnitude in terms of injected volume will give insights into upscaling of induced seismicity from underground laboratory scale to field scale.

Micro- to nano-sized solid inclusions in magnetite record skarn reactions

Abstract. Magnetite is a widespread ore mineral in skarn systems and usually hosts a wide variety of inclusions. Micro- to nano-sized solid inclusions in magnetite are unique tools to track the evolutionary processes of its host mineral and, subsequently, to constrain the timing of the mineralization event. In this study, we characterize micro- to nano-sized solid inclusions in magnetite from the La Víbora magnesian skarn (Málaga, Spain) using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). TEM energy-dispersive X-ray spectrometry (EDS) analyses and compositional mapping expose two types of nano-inclusions oriented along the (111) of magnetite: type 1 includes dolomite, spinel, and Mg–Fe–Al silicate, and type 2 is made up of Mg–Fe–Al silicates enveloping the Mg-bearing amorphous silica phase. High-resolution transmission electron microscopy (HRTEM), selected-area electron diffraction (SAED), and fast Fourier transform (FFT) patterns reveal that the majority of the solid inclusions display non-oriented matrices compared to the host magnetite, precluding the possibility of sub-solidus processes. Instead, these inclusions are thought to preserve skarn mineral assemblages that were entrapped during the growth of magnetite. However, the local supersaturation of fluids trapped in the boundary layer of crystallizing magnetite is evidenced by coherent lattice orientation of precipitated and host magnetite and by the occurrence of an Mg-bearing amorphous silica phase. Our findings reveal that skarn reactions observed at field and microscopic scales are also recorded in nano-sized inclusions within magnetite. These observations underscore the significance of micro- to nano-scale solid inclusions in magnetite to decipher overprinted skarn reactions as well as constraining the timing of Fe mineralization events in skarns.

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Field scale partitioning of Landsat land surface temperature into soil and canopy components for evapotranspiration assessment using a two-source energy balance model

Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models

AbstractNear real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.

Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern field-scale heterogeneities, and estimates’ accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

The LTAR Cropland Common Experiment at the Kellogg Biological Station.

The Kellogg Biological Station Long-term Agroecosystem Research site (KBS LTAR) joined the national LTAR Network in 2015 to represent a northeast portion of the North Central Region, extending across 76,000 km2 of southern Michigan and northern Indiana. Regional cropping systems are dominated by corn (Zea mays)-soybean (Glycine max) rotations managed with conventional tillage, industry-average rates of fertilizer and pesticide inputs uniformly applied, few cover crops, and little animal integration. In 2020, KBS LTAR initiated the Aspirational Cropping System Experiment as part of the LTAR Common Experiment, a co-production model wherein stakeholders and researchers collaborate to advance transformative change in agriculture. The Aspirational (ASP) cropping system treatment, designed by a team of agronomists, farmers, scientists, and other stakeholders, is a five-crop rotation of corn, soybean, winter wheat (Triticum aestivum), winter canola (Brassicus napus), and a diverse forage mix. All phases are managed with continuous no-till, variable rate fertilizer inputs, and integrated pest management to provide benefits related to economic returns, water quality, greenhouse gas mitigation, soil health, biodiversity, and social well-being. Cover crops follow corn and winter wheat, with fall-planted crops in the rotation providing winter cover in other years. The experiment is replicated with all rotation phases at both the plot and field scales and with perennial prairie strips in consistently low-producing areas of ASP fields. The prevailing practice (or Business as usual [BAU]) treatment mirrors regional prevailing practices as revealed by farmer surveys. Stakeholders and researchers evaluate the success of the ASP and BAU systems annually and implement management changes on a 5-year cycle.

Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv’/Fm’), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties.

Monitoring Crop Condition at Gield Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR)

Operations that rely on optically-based Normalized Difference Vegetation Index (NDVI) to track crop productivity, are negatively impacted by cloud cover. In this research, parameters from Synthetic Aperture Radar (SAR) imagery were calibrated to NDVI with the targeted outcome to integrate SAR into Canadian crop monitoring services. Data from fully polarimetric RADARSAT-2 and dual-polarization Sentinel-1B imagery were calibrated to Sentinel-2 NDVI. Random Forest Regression (RFR) and Least Squares Boosting (LSBoost) were tested to calibrate SAR to NDVI (SARcal-NDVI) for six crops (corn, canola, soybeans, wheat, oats and barley). RADARSAT-2 provided the best fit with R2 exceeding 0.84 for canola, 0.87 for wheat and 0.90 for corn and soybeans. Correlations were lower for oats (R2 of 0.72–0.77) and barley (R2 of 0.43–0.64) due to a limited number of fields. Although a global model yielded lower correlations (R2 of 0.80), this model would be easier to implement into operations. Total power, the first and second eigenvalues and VH backscatter were important in reducing model error. The SARcal-NDVI and Growing Degree Days were then integrated into a Crop Structure Dynamic Model to produce daily estimates of crop condition, at field scales. The next step is to test if these daily estimates of crop condition can improve crop yield forecasting for the Canadian agriculture sector.

Impact of Inhomogeneous Deformation of Shale Matrix on Gas Depletion Behavior at Field Scale: A Modified Triple-Porosity Model

Impact of Inhomogeneous Deformation of Shale Matrix on Gas Depletion Behavior at Field Scale: A Modified Triple-Porosity Model

Introduction of an optimised dynamic installation anchor via model test and numerical analysis: Freefall in the water column

This paper introduces a novel shaped dynamically installed anchor (called ‘maverick anchor’) with optimised geometric components (tip, shaft, and fins). The performance of the maverick anchor in terms of hydrodynamic characteristics (terminal velocity, hydrodynamic drag, and directional stability) obtained through freefall drop tests in water and Computational Fluid Dynamics (CFD) analyse was compared with that of existing dynamically installed anchors used in offshore Brazil and in the Gulf of Mexico. Freefall drop tests on model anchors were performed in a 1.56 m depth water tank at UWA. Freefall motions of the anchors were filmed mounting two cameras, and subsequently quantified using Python code. In testings, geometric similarity was ensured between the model and prototype. CFD analyses were then carried out on the corresponding field scale anchors using Ansys/Fluent, fixing the anchor at a particular location, and flowing the water column with a velocity. Numerical analyses ensured similarity in Reynolds number. Results have confirmed that compared to the commercially used dynamically installed anchors, the maverick anchor can attain a higher terminal velocity, and the corresponding drag coefficient is lower due to smooth fluid flow along the anchor body. The verticality or lateral stability during the freefall was also promising.

Environmental and socio-economic evaluation of a groundwater bioremediation technology using social Cost-Benefit Analysis: Application to an in-situ metal(loid) precipitation case study

Bioremediation can be an alternative or complementary approach to conventional soil and water treatment technologies. Determining the environmental and socio-economic impacts of bioremediation is important but rarely addressed. This work presents a comprehensive sustainability assessment for a specific groundwater bioremediation case study based on In-situ Metal(loid) Precipitation (ISMP) by conducting a social Cost-Benefit Analysis (CBA) using two different approaches: environmental Life Cycle Costing (eLCC) and Impact Pathway Approach (IPA). Externalities are calculated in two ways: i) using Environmental Prices (EP) to monetize Life Cycle Assessment (LCA) results and metal(loid)s removed at field scale, and ii) following the IPA steps to determine the social costs avoided by removing arsenic contamination at full scale. The results show that, in the baseline scenario, the project is not socio-economically viable in both cases as the Net Present Value (NPV) is −129,512.61 € and − 415,185,140 € respectively. Sensitivity and scenario analyses are performed to identify the key parameters and actions needed to reach a positive NPV. For instance, increasing the amount of water treated per year to 90 m3 and assuming a 20 % increase in operation costs and a 60 % increase in construction costs can make the project socio-economically viable at the field scale, while a reduction in the social discount rate from a 4 % to a 2 % can lead to a positive NPV at the full scale. The approaches proposed in this work may be useful for practitioners and policymakers when evaluating the environmental and socio-economic impacts of bioremediation technologies at different scales and regions, as well as human health impacts caused by contaminants at the current legal limits.