Inventory Data Research Articles

Dataset on the life cycle assessment of the production of stabilized lactic acid bacteria.

Dataset on the life cycle assessment of the production of stabilized lactic acid bacteria.

Modelling multi-layer fine fuel loads in temperate eucalypt forests using airborne LiDAR and inventory data

Modelling multi-layer fine fuel loads in temperate eucalypt forests using airborne LiDAR and inventory data

Modeling the growth and yield of natural hardwood stands in the southern United States using the Forest Inventory and Analysis data

Modeling the growth and yield of natural hardwood stands in the southern United States using the Forest Inventory and Analysis data

Dataset of observables for small modular lead-cooled fast reactor MOX spent nuclear fuel.

Dataset of observables for small modular lead-cooled fast reactor MOX spent nuclear fuel.

Life cycle inventory data generation for yogurt packaging in Austria

Life cycle inventory data generation for yogurt packaging in Austria

Identification of groundwater potential zones using geospatial analysis and frequency ratio model: the case of Kulfo-Hamassa watershed, Rift Valley, Ethiopia

Groundwater potential mapping is crucial for ensuring the sustainable use of groundwater resources. This study aims to identify groundwater potential zones in the Kulfo-Hamassa watershed, located in the Rift Valley, Ethiopia, using geospatial and frequency ratio (FR) approaches. A training set (70%) and a testing set (30%) were created from water well inventory data. Factors such as elevation, slope, curvature, topographic wetness index (TWI), stream power index (SPI), lithology, soil texture, land use/land cover, rainfall, lineament density, and drainage density were selected based on data availability, literature review, and expert consultation. The FR method was used to analyze the statistical relationships between these factors and groundwater occurrence. Groundwater potential zones were identified and categorized into three zones, including low (5.03 km2, 5.03%), moderate (950.21 km2, 40.61%), and high (1272.04 km2, 54.36%) potential zones. Validation using receiver operating characteristic (ROC) curves produced an area under the curve (AUC) value of 0.831, indicating high model accuracy. The quantitative validation showed a 76.3% agreement between predicted and actual well yields. This groundwater potential map provides a practical tool for site selection and sustainable water management, benefiting decision-makers, practitioners, and local communities involved in water resource planning in the watershed.

Combining long-term ICP Forests monitoring data with the Yasso carbon cycling model at the European scale

Forests form a major organic carbon reservoir, both above- and belowground. In the course of global change, predicting possible changes in these carbon reservoirs is essential. To this end, the Horizon Europe PathFinder project aims to develop an innovative forest monitoring system allowing consistent EU greenhouse gas reporting of LULUCF (Land Use, Land Use Change & Forestry) in combination with advanced policy pathway assessments. Greenhouse gas reporting of soil organic carbon (SOC) stock changes in forests commonly relies on simulations by soil carbon cycling models, such as Yasso (Y20), which uses only climate data and soil carbon inputs that can be derived by country-specific approaches from National Forest Inventories. However, the agreement between measured versus simulated carbon stocks and changes at the European scale has not yet been established. Within the framework of this project, this study aims to derive European-wide harmonised soil carbon inputs and stock estimates since the 1990s and further develop the current estimation methodology. After exploration of the available data sets, the ICP Forests Level II forest condition monitoring database was found the most suitable to set the initial modelling conditions. It is the only harmonised data set at the European scale that comprises above- and belowground compartments and contains repeated assessments on a subset of about 200 plots across Europe. The pre-processing of the observed data on soil carbon stock, growth and litterfall from the central ICP Forests database was very labour-intensive. As part of the ICP Forests monitoring programme, carbon concentrations and bulk densities are measured down to a depth of 80 cm. Using mass-preserving splines, soil carbon stocks were estimated down to a depth of 100 m to make them comparable with Y20. Regression models were developed to estimate litterfall inputs based on forest inventory data. We simulated SOC stocks by Y20 in ICP Forests Level II plots with available stand inventory data and soil characterization. Soil carbon inputs were obtained using two approaches: an inventory approach, with litterfall estimated by the above-mentioned regression models, and root and coarse-woody inputs by allometric functions, and a satellite approach, with net primary production (NPP) from MODIS at 500 m resolution. The Y20-simulated SOC stocks were compared with the SOC stocks to 100 cm depth based on the soil inventory data. an inventory approach, with litterfall estimated by the above-mentioned regression models, and root and coarse-woody inputs by allometric functions, and a satellite approach, with net primary production (NPP) from MODIS at 500 m resolution. The Y20-simulated SOC stocks were compared with the SOC stocks to 100 cm depth based on the soil inventory data. On average, the satellite approach estimated higher soil carbon inputs than the inventory approach (+20%). The SOC stocks simulated by Y20 were overall in line with observed SOC stocks. The simulations for broadleaf-dominated stands agreed well with SOC measurements, with average deviations below 1 kg C m-2 using the satellite approach. In coniferous stands, Y20-simulated SOC stocks were lower than observed by 3-5 kg C m-2. This is likely due to the intrinsic soil properties driving SOC storage and stabilization in highly acidic, coniferous forests (i.e. Podzols and Umbrisols), which are not accounted for in Y20.

Development of ecosystem processes, functions and biodiversity in new forests on post-agricultural land

Afforestation of post-agricultural land offers a promising nature-based solution to address climate change and biodiversity loss. However, understanding the long-term effects of land-use legacies on ecosystem processes and functions in these new forests requires a comprehensive integration of field-based measurements and advanced technologies. This study combines forest inventory data, biodiversity surveys, and soil sampling with remote sensing (LiDAR) and environmental DNA (eDNA) to assess forest structure (FS), biodiversity, and carbon dynamics in forests established on former agricultural land. We conducted a chronosequence study in beech (Fagus sylvatica), oak (Quercus robur), and spruce (Picea abies) stands planted over the past 50 years in the same afforestation area in Denmark. Ground truth data were collected within a circular plot of 15 m radius and included forest inventory, understory vegetation and soil fungal community composition from 2022, while forest floor carbon and mineral soil organic carbon (SOC) stock was from the years 1998, 2011 and 2022. FS was analysed using high-resolution national LiDAR data under leaf-off conditions from 2019, while the PacBio sequencing technology for eDNA analyses was employed to explore belowground fungal diversity and community composition. Preliminary findings suggest that FS and biodiversity are shaped by a combination of tree species types and stand age. Spruce showed rapid vertical development with dense canopy cover, while oak forests supported higher structural heterogeneity and tree species richness due to their multi-layered canopy architecture and light conditions, supporting colonization of other woody species. Beech forests exhibited significant vertical heterogeneity at later stages but tended to develop a more homogeneous structure over time. The development in understory plant and belowground fungal communities reflected land-use legacies, showing a gradual yet slow, recovery over time. In general, open habitat understory species disappeared with canopy closure and forest specialist species slowly increased over time, while generalist species remained abundant in all surveys. Dispersal limitations emerged as a primary constraint shaping the vegetation. Fungal communities transitioned more rapidly, becoming dominated by ectomycorrhizal and basidiomycete species. Forest floor carbon sequestration followed a non-linear temporal trend, stabilizing after about 30 years, suggesting higher SOC stocks under spruce compared to oak. The mineral soil C stocks increased with forest age across the three soil inventories and sequestrated 0.29 ± 0.05 Mg C ha-1 per year, where spruce exhibited the highest rates of soil carbon accumulation and biomass over time. This study emphasizes the value of combining traditional ecological measurements with emerging technologies to better understand the complex interactions driving forest development on post-agricultural landscapes. The findings highlight the potential of afforestation to support biodiversity recovery, carbon sequestration, and ecosystem functioning, while emphasizing the long-term influence of agricultural legacies on forest ecosystems.

A comprehensive analysis of landslide susceptibility in Iyidere Basin (NE, Turkey) using machine learning techniques and statistical bivariate methods

Abstract Natural events are called disasters when they cause great damage, human suffering, or loss of life. Landslides, one of these disasters, cause significant damage to property and infrastructure and pose risks to people's lives. In this research, landslide susceptibility was studied in Iyidere Basin, located in northeastern Turkey. This basin, which is among the cities where the most landslide events occur in Turkey, is a very important representative area in terms of a comprehensive analysis of landslides in the region. Bivariate (frequency ratio, weight of evidence, statistical index) and machine learning methods (artificial neural network, logistic regression) were used to evaluate landslide susceptibility with fifteen environmental parameters and 588 landslide inventory data. Landslide inventory data was generated using different sources, and environmental parameters databases were created using various sources and software. A receiver operating characteristic curve and Kappa statistic value were generated to test the performance and reliability of the susceptibility maps. It was determined that landslide susceptibility is higher in the downstream part of the basin. Although it varies between methods, it has been determined that approximately one-quarter of the basin has high and very high landslide susceptibility. The most effective parameters (drainage density, slope, curvature, lithology, land cover, distance to stream, and roads) for susceptibility and their classes were revealed.

Study on Forest Growing Stock Volume in Kunming City Considering the Relationship Between Stand Density and Allometry

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 108 m3, which closely aligns with contemporaneous statistical records. The model yielded an R2 of 0.727, an RMSE of 537.566 m3, and a MAE of 239.767 m3, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R2 from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area.

Relationships between Sociosexuality and Dermatoglyphic Traits

In humans, prenatal development of brain dispositions to sex differences in mating behavior is difficult to study directly. Indirect prenatal markers, including dermatoglyphics, present a viable option. In this study we tested a hypothesis that some radio-ulnar contrasts in dermatoglyphic ridge counts could be related with human sociosexuality. Sociosexual Orientation Inventory (SOI) data from 180 young adults, along with fingerprints of their terminal phalanges (via hand scanning) were collected, and relationships between SOI and dermatoglyphics were analyzed. Typical sex differences in SOI were recorded with higher scores in males and lower in females. Among other results we found that on the index finger lower number of triradii and cores (i.e., mostly in loop type dermatoglyphic patterns) and radial-biased within-finger asymmetry in ridge counts typical for ulnar loops were connected with typical sex differences in SOI (higher in males and lower in females) while in subjects possessing an opposite dermatoglyphic arrangement – higher numbers of cores and triradii and ulnar-biased within-finger ridge count asymmetry typical in radial loops – sex differences in SOI scores disappeared. Recognized significant and systematic trends were mostly connected with variables derived from dermatoglyphic features on the 2nd and 4th fingers. Possible relationships with prenatal androgen causation are discussed.

Uncertainty in inventories for life cycle assessment: State‐of‐the‐art, challenges, and new technologies

AbstractUncertainty is a critical factor that can hinder the quality and potential applications of life cycle assessment (LCA) results. A prominent source of uncertainty stems from the life cycle inventory (LCI) data. Various methodologies exist to estimate the uncertainty associated with LCI data, primarily based on the widely used structured pedigree matrix approach or the computationally intensive Monte Carlo simulation. This perspective review explores how new technologies (e.g., computational algorithms and data collection methods) from data science and related fields can contribute to identifying, quantifying, and reducing uncertainty in LCI modeling. A brief overview of the sources of uncertainty in LCI modeling and how they are addressed in current LCA practice is provided. Additionally, several new technologies are identified, and the potential benefits of their implementation in reducing uncertainties in LCI modeling are discussed. This perspective review concludes by identifying potential areas that require further development for these technologies.

Environmental impact assessment of maize cultivation system considering different irrigation methods

Maize is a key crop for the livestock sector being able to produce different fodder. Among these, ear maize silage is widely used as an energy source in the diets of pigs, dairy cows and fattening cattle. Given the variability of rainfall, irrigation plays a relevant role to achieve both satisfactory productivity and product quality. In this context, it is essential to explore the sustainability of different irrigation methods for maize cultivation.In this study, Life Cycle Assessment (LCA) was applied to evaluate the environmental impact of maize farms using different irrigation systems: pivot, drip, flood, and hose irrigation. One ton of ear maize silage at 48% moisture content was selected as functional unit and a “from cradle to farm gate” was considered as system boundary . Primary inventory data were collected mainly by surveys and interviews with the farmers. The Environmental Footprint 3.1 method was used to assess 14 impact categories. The results do not allow to clearly identify the best irrigation method across all environmental impact categories, therefore highlighting the need of trade-offs. While yield is the primary driver of environmental impacts, the influence of irrigation remains significant. Climate change was found to range from 116.66 kg CO2 eq./t of ear maize for flood irrigation to 207.42 kg CO2 eq./t for hose irrigation. Water use varied from 2178.29 m³ depriv./t for pivot irrigation to 10380.65 m³ depriv./t for flood irrigation. Regarding the contribution analysis, changing the considered environmental impact the main contribution varies, for example nitrous oxide is the main responsible to climate change, ammonia to particulate matter and acidification while nitrate and ammonia emissions to marine eutrophication. In conclusion, this study provides a basis for evaluating different irrigation methods, emphasising that irrigation plays a significant role in the overall environmental impact of maize cultivation, regardless of the end product.

Materials management under conditions of uncertainty: Does it matter much in healthcare?

ABSTRACT Materials management in healthcare has considerably widened in scope. In addressing the research question in the title of this paper, we review the pertinent literature and analyze healthcare inventory modeling. In determining the proper quantity of materials to meet patient demand without incurring unnecessary costs, we find that uncertainty in healthcare inventory owes predominantly to aggregate demand. Stock-out cost arises as inventory is exhausted by much higher or inadequately forecasted demand, inefficiently replenished stock, insufficient safety stock, and cash flow challenges in procurement. Overstock cost can result from reduced demand that racks up inventory costs and leaves healthcare organizations with expired or obsolete inventory, further risking their bottom line. Absent supply chain disruptions, the organizational propensity to purchase, when prices are low, can be contained by inventory models whose assumptions of essentially constant and deterministic demand we examine. We suggest that optimal stock calculations blend approaches and measures in finance and operations management and account for limitations of stock-out, overstock, and pricing illustrative of uncertainty. Materials managers can thus use historical order, inventory, and other relevant data and evaluate financial risks more efficiently, given that managing inventory is based on the specific material or product, and uncertainty is practically the norm in healthcare.

Ensemble Learning-Based Approach for Forecasting Inventory Data in Prefabricated Component Warehousing

Accurately predicting the storage area of prefabricated components facilitates transshipment scheduling and prevents the waste of storage space. Due to the influence of numerous factors, precise prediction remains challenging. Currently, limited research has addressed the prediction of storage areas for prefabricated components, and effective solutions are lacking. To address this issue, a GRU model with an attention mechanism based on ensemble learning was proposed. The model employed the Bo-Bi-ATT-GRU approach to address the time series prediction of storage areas. A Bayesian optimization algorithm was utilized to enhance parameter tuning and training efficiency, while an ensemble learning framework improved model stability. In this study, a port container dataset was used for experimentation, with root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation metrics. Compared with the GM model, the R2 of the proposed model improved by 3.38%. Experimental results demonstrated that the ensemble learning-based prediction model offered superior performance in forecasting the storage area of prefabricated components.

Grasping at water: a gap-oriented approach to bridging shortfalls in freshwater biodiversity conservation.

Freshwater biodiversity is the fastest declining part of the global biota, threatened by multiple stressors including habitat loss and fragmentation, climate change, invasive species, water pollution, and abstraction by humans. A multitude of recent agenda-setting publications have pointed out key objectives and goals for addressing this freshwater biodiversity crisis, but important gaps must be overcome to reach ambitious conservation targets. In this perspective, we complement these high-level papers in freshwater conservation by highlighting important gaps in knowledge, governance, and implementation. This gap-oriented approach is designed to facilitate meaningful action by highlighting missing 'pieces' in the conservation process, and their connection to existing and emerging solutions in the literature. We derive 13 overarching gaps from a conference session and informal synthesis of recent literature in freshwater biodiversity conservation to catalyse research, advocacy, and action to meet freshwater goals for the post-2020 Kunming-Montreal Global Biodiversity Framework (GBF). Key gaps include inventory data on global freshwater biodiversity, collating and mobilizing conservation evidence in practice, improving coordination of ecological governance at scale -including within and across catchments-and navigating trade-offs between economic development, resource consumption, and priorities for freshwater biodiversity. Finally, we apply this gap-oriented approach to key language describing GBF goals for freshwater biodiversity conservation, and point out existing and emerging solutions which may help address important gaps. Major themes that address multiple gaps include the use of Nature-based Solutions and Other Effective Area-based Conservation Measures (OECMs), navigation of water management trade-offs between human and environmental needs, co-production of knowledge with Indigenous and local people and other stakeholders, integration of conservation research and action between aquatic and terrestrial ecosystems, and funding and policy mechanisms to facilitate conservation action and support meaningful monitoring of conservation evidence across hydrological scales.

Life Cycle Assessment of a PCM-Filled Compact Storage Module for Building Applications

This study performs a Life Cycle Assessment on the production of a commercial PCM building application developed by RUBITHERM to quantify its environmental impacts and identify environmental hotspots across manufacturing, unlocking climate change mitigation potential. This research adds to the consideration of embodied energy demands and emissions when developing building efficiency solutions, especially for innovative material applications where knowledge is limited. The building application under examination is a compact storage module, consisting of an aluminium case filled with salt hydrate PCM, with a targeted performance of 94 Wh heat storage capacity. The LCA of the manufacturing stage resulted in a climate change impact of 5.81 kg CO2 eq. The research showed that the aluminium of the case to be filled in with PCM is the main contributor to almost all impact categories addressed, including climate change, while the sensitivity analysis revealed that the total climate change of the final product is highly dependent on the recycled aluminium content, which could be decreased by 46% by increasing the new scrap and post-consumer scrap aluminium streams. Finally, the study provides detailed Life Cycle Inventory data, based on real data shared by RUBITHERM, and methodology transparency to facilitate built-up research in the field.

Blood Donation Management System

ABSTRACT The Blood Bank Management System (BBMS) is an essential digital system designed to address the problems of emergency unavailability, blood scarcity, and manual record-keeping inefficiencies. This study provides a systematic approach to automating blood bank operations, including donor registration, blood collection tracking, and real-time availability updates. The proposed approach lowers the likelihood of delays and human error by guaranteeing the precision, effectiveness, and speedy retrieval of blood inventory data. By utilising modern computing technologies, the system expedites reporting procedures, enhances donor-recipient matching, and speeds up emergency blood requests. Real-time donor availability updates and a mobile application further improve accessibility and responsiveness. Future advancements will include predictive analytics and geolocation-based donor search capabilities to enhance blood supply chain management. This study highlights the impact of digital transformation in healthcare as well as the need for automated, scalable solutions to improve blood donation and transfusion services.

Psychosoziale Notfallversorgung in Unternehmen – Ergebnisse einer Bestandsaufnahme

Psychosocial emergency care in companies – Results of an inventory Objective and Data Basis: The lack of scientific insights into organizational models and approaches for psychosocial support during workplace emergencies prompted a research project at SRH University, supported by DGUV. The study explored perspectives from companies, accident insurance providers, voluntary PSNV-B teams, and external service providers across four sub-projects. Methods: Eight qualitative and quantitative studies were conducted, each with a screening phase and an in-depth survey. In addition, a secondary data analysis of an existing data set was carried out. Results: Organizations face a diverse range of emergencies, for which partial preventive measures in psychosocial emergency care (PSNV) have been implemented. There is considerable potential for improvement, particularly in organizational and individual measures, as well as in documentation and reporting practices. Employees affected by emergencies report a lack of specific preparedness, limited documentation, insufficient acute-phase support, and deficits in follow-up care. Accident insurance providers offer a wide array of services for psychosocial emergency care, emphasizing the importance of tailored prevention and rehabilitation measures. They also highlight the need for enhanced information provision and management systems. One in every four to six missions of voluntary PSNV-B teams had a workplace-related cause. Such missions are considered more complex, requiring additional personnel and specialized training. Six months post-intervention, up to 50 % of the individuals supported in workplace contexts reported clinically significant symptoms and experiencing limitations in their professional lives. External service providers vary in size, organizational structure, and the scope of their services. However, there is a lack of consistent quality criteria concerning provider qualifications, ongoing training, and the standards upon which services are based. Conclusions: With scientifically proven effective measures, companies could be supported even better and more clearly in establishing good emergency prevention and in providing good psychosocial emergency care for employees when an emergency occurs. Keywords: psychosocial emergency care – occupational emergencies – mental health hazards – occupational accidents – prevention

Diversity, stand structure and ecosystem services of Ouagadougou regional urban forest, Burkina Faso

Urban forests are key natural solutions for biodiversity conservation in West Africa. However, increasing urban population negatively affects the ecological and social attributes of urban forests. This study assessed the woody diversity, stand structure, ecosystem services, and management of the Ouagadougou Regional Urban Forest in Burkina Faso. Forest inventory data were collected in 40 plots of 1000 m2, following a stratified sampling design based on the three vegetation types in the forest. Ecosystem services and forest management were assessed through semi-structured interviews with 120 informants. Data were analyzed using diversity attributes, structural attributes and ethnobotanical indicators. The findings showed a γ-diversity of 55 woody species from 44 genera and 20 families, distributed in tree savannas, shrub savannas and shrub steppes. Diversity attributes such as species richness, Shannon’s index, Pielou’s index, Simpson’s index as well as structural attributes i.e. stem DBH, tree height, basal area, tree density indicated significant variations between the three vegetation types (p-value < 0.05). The distribution of diameter and height classes revealed unstable populations associated with poor regeneration potential and recruitment problems. Ecosystem services included provisioning (77.5%), regulating (29.17), cultural (21.67%), and supporting (7.27%) services. However, the surrounding populations recognized the severe degradation of the forest due to agriculture, wood cutting, waste depositing, and settlements installation. This study showed that the regional urban forest contributes to biodiversity conservation and livelihood supports, but undergoes degradation, resulting in declining population dynamics and poor regeneration potential. As a provider of many ecosystem services, the regional urban forest must benefit from more restrictive policies and legislation on the management of urban green spaces. These policies must be strengthened in order to reduce the effects of urbanization on the forest.