Now showing items 46660-46679 of 49060

    • U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

      Guk-Jin Son; Dong-Hoon Kwak; Mi-Kyung Park; Young-Duk Kim; Hee-Chul Jung (MDPI AG, 2021-12-01)
      Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.
    • U.S. Almond Exports and Retaliatory Trade Tariffs

      Abraham Ajibade; Sayed Saghaian (MDPI AG, 2022-05-01)
      The U.S. is the top producer, exporter, and consumer of tree nuts in the world. Tree nuts are a significant part of U.S. agricultural exports to the world. In 2019, the U.S. exported about USD 9.1 billion worth of tree nuts, just behind soybean exports at USD 18.7 billion. Tree nuts, such as almonds and pistachios, are mostly produced in the state of California. California produces 100% of U.S. commercial almonds. Globally, almonds are the leading U.S. tree nut export in both value and volume. Almonds are shipped to over 90 countries annually. This study aimed to investigate factors affecting the export demand function for U.S. almonds in major destination countries and evaluate the impact of the retaliatory trade tariffs policy by some of the importing countries on the U.S. almond exports. The currently available literature does not fully address these issues. We identified the top five almond export destinations, which were in Europe and Asia, namely, China/Hong Kong, Germany, India, Japan, and Spain, which account for more than 50% of U.S. almond imports. We used a double-log export demand equation that is well referenced in the literature and economic theory to identify the significant explanatory variables affecting the U.S. almonds export demand function. We also tried to estimate the impact of retaliatory tariffs on almond exports imposed by the major importing countries. Our results showed that U.S. almond and pistachio prices, real exchange rates, and gross domestic products of importing countries were significant factors that affected U.S. almond exports. The results showed that the imposed retaliatory tariffs had no negative effect on U.S. almond exports. This could have been because the study ended in 2019 and did not involve enough data to fully evaluate the impact of the retaliatory trade tariffs policy. U.S. almond exports have market concentration and strong market power in international markets. The efforts toward more sustainable production of almonds to solidify an already established market share in the world almond markets and against substitutes, such as pistachios, seem to be a sound strategy and focus of the U.S. almond agribusinesses and exporters.
    • U.S. Consumer Attitudes toward Antibiotic Use in Livestock Production

      Syed Imran Ali Meerza; Sabrina Gulab; Kathleen R. Brooks; Christopher R. Gustafson; Amalia Yiannaka (MDPI AG, 2022-06-01)
      Antimicrobial resistance, which decreases the efficacy of antibiotics and other antimicrobials, has led to concerns about the use of antibiotics in livestock production. Consumers play an important role in influencing producers’ decisions about the use of antimicrobials through their choices in the marketplace, which are driven by attitudes toward these practices. This study examines consumers’ levels of concern about (and acceptance of) the use of antibiotics in livestock production for four objectives: to treat, control, and prevent infections, and to promote growth. Results reveal that the majority of respondents were highly concerned about antibiotic use to promote growth in livestock production and considered this use to be unacceptable. Participants with higher objective knowledge of antibiotic resistance and antibiotic use in livestock production were more likely to accept antibiotic use to treat and control disease, but less likely to accept its use to prevent disease or to promote growth. Participants with high levels of trust in the livestock industry were more likely to accept antibiotic use to control and prevent infections and to be neutral about antibiotic use to promote growth in food animals. Respondents who believed that antibiotic use decreases animal welfare were more likely to be very concerned about antibiotic use to treat, prevent, and control disease, and less likely to accept antibiotic use to treat diseases in food animals. The study findings should be of interest to producers considering the adoption of sustainable technologies and production practices, food retailers making procurement decisions, and policymakers identifying policies that can alleviate antimicrobial resistance in the agri-food sector.
    • U.S. Demand for Organic and Conventional Fresh Fruits: The Roles of Income and Price

      Travis A. Smith; Chung L. Huang; Steven T. Yen; Biing-Hwan Lin (MDPI AG, 2009-08-01)
      Using retail purchase data reported by Nielsen’s Homescan panel this study investigates the U.S. demand for organic and conventional fresh fruits. The study fills an important research void by estimating the much needed income and price elasticities for organic and conventional fruits utilizing a censored demand approach. Household income is found to affect organic fruit consumption. Consumers are more responsive to price of organic fruits than to price of conventional fruits. Cross-price effects suggest that a change in relative prices will more likely induce consumers to “cross-over” from buying conventional fruits to buying organic fruits, while it is less likely that organic consumers will “revert” to buying conventional fruits.
    • U.S. Interest Rate and Household Debt Sustainability: The Case of Korea

      Jong Chil Son; Hail Park (MDPI AG, 2019-07-01)
      This paper revisits the issue of household debt sustainability in Korea responding to changes in U.S. interest rates. We investigate not only the transmission channels from U.S. interest rates to domestic interest rates, using the Bayesian VAR (vector autoregression) model, but also the issue of identifying households that are vulnerable in terms of their debt repayments, and we execute projections for the upcoming years given conditional forecasts and various macroeconomic scenarios. The estimation results indicate that first, the domestic policy rate will likely increase and then stagnate conditionally on the path of the U.S. policy rates. Second, the ratios of vulnerable households over total indebted households, which has been growing since 2012, will likely expand mildly over the upcoming years given an approximately 1.6%p gradual increase in interest rates and stable macroeconomic environments. Finally, however, the projected trend of domestic interest rates can cause a rapid expansion in the ratios of vulnerable households, in conjunction with a series of combined negative shocks such as highly concentrated principal repayment schedules, sharp declines in housing prices, and the occurrence of a crisis.
    • U.S. Potential of Sustainable Backyard Distributed Animal and Plant Protein Production during and after Pandemics

      Theresa K. Meyer; Alexis Pascaris; David Denkenberger; Joshua M. Pearce (MDPI AG, 2021-04-01)
      To safeguard against meat supply shortages during pandemics or other catastrophes, this study analyzed the potential to provide the average household’s entire protein consumption using either soybean production or distributed meat production at the household level in the U.S. with: (1) pasture-fed rabbits, (2) pellet and hay-fed rabbits, or (3) pellet-fed chickens. Only using the average backyard resources, soybean cultivation can provide 80–160% of household protein and 0–50% of a household’s protein needs can be provided by pasture-fed rabbits using only the yard grass as feed. If external supplementation of feed is available, raising 52 chickens while also harvesting the concomitant eggs or alternately 107 grain-fed rabbits can meet 100% of an average household’s protein requirements. These results show that resilience to future pandemics and challenges associated with growing meat demands can be incrementally addressed through backyard distributed protein production. Backyard production of chicken meat, eggs, and rabbit meat reduces the environmental costs of protein due to savings in production, transportation, and refrigeration of meat products and even more so with soybeans. Generally, distributed production of protein was found to be economically competitive with centralized production of meat if distributed labor costs were ignored.
    • U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

      Hamidreza Zoraghein; Brian C. O’Neill (MDPI AG, 2020-04-01)
      Spatial population distribution is an important determinant of both drivers of regional environmental change and exposure and vulnerability to it. Spatial projections of population must account for changes in aggregate population, urbanization, and spatial patterns of development, while accounting for uncertainty in each. While an increasing number of projections exist, those carried out at relatively high resolution that account for subnational heterogeneity and can be tailored to represent alternative scenarios of future development are rare. We draw on state-level population projections for the US and a gravity-style spatial downscaling model to design and produce new spatial projections for the U.S. at 1 km resolution consistent with a subset of the Shared Socioeconomic Pathways (SSPs), scenarios of societal change widely used in integrated analyses of global and regional change. We find that the projections successfully capture intended alternative development patterns described in the SSPs, from sprawl to concentrated development and mixed outcomes. Our projected spatial patterns differ more strongly across scenarios than in existing projections, capturing a wider range of the relevant uncertainty introduced by the distinct scenarios. These projections provide an improved basis for integrated environmental analysis that considers uncertainty in demographic outcomes.
    • U.S. Sustainable Food Market Generation Z Consumer Segments

      Ching-Hui (Joan) Su; Chin-Hsun (Ken) Tsai; Ming-Hsiang Chen; Wan Qing Lv (MDPI AG, 2019-06-01)
      This study explores the interaction between environmental consciousness and sustainable food attributes as predictors in the market segmentation process for sustainable foods with respect to United States (U.S.) Generation Z (Gen Z) consumers. This study was executed using a cross-national, web-based survey to analyze and categorize Gen Z female (n = 435) and male (n = 377) consumers between 18 and 23 years of age living in the continental United States. The objectives of this study were to classify U.S. Gen Z consumers into unique segments based on their environmental consciousness and to assess the functional relationships among the following: (a) their degree of ecological awareness; (b) the importance of the perception of sustainable food attributes; (c) their food choices associated with healthy eating habits; and (d) sociodemographics. Survey data were analyzed using cluster analysis of consumer groups based on environmental consciousness. Environmental consciousness was measured using a composite score of the environmental involvement scale and the environmental values scale. Gen Z consumers with high environmental consciousness (sustainable activists) and moderate ecological awareness (sustainable believers) considered more eco-friendly and healthy product attributes when purchasing sustainable food, whereas Gen Z consumers with low environmental consciousness (sustainable moderates) considered more extrinsic product attributes (e.g., price and convenience). Furthermore, the results indicate that food choices associated with healthy eating habits could be used to develop a profile for different eco-conscious Gen Z consumer groups. The contributions of this study are twofold. First, for academic researchers, this paper extends marketing segmentation research concerning environmentally sensitive young consumers. Second, for industry professionals, this study provides food retailers or food service operators with sustainable consumer values that will aid in the development of effective, green marketing strategies to better attract and meet the sustainability expectations of Gen Z—the consumer segment with the most spending power of any generation.
    • UASB followed by Sub-Surface Horizontal Flow Phytodepuration for the Treatment of the Sewage Generated by a Small Rural Community

      Massimo Raboni; Renato Gavasci; Giordano Urbini (MDPI AG, 2014-10-01)
      The paper presents the results of an experimental process designed for the treatment of the sewage generated by a rural community located in the north-east of Brazil. The process consists of a preliminary mechanical treatment adopting coarse screens and grit traps, followed by a biological treatment in a UASB reactor and a sub-surface horizontal flow phytodepuration step. The use of a UASB reactor equipped with a top cover, as well as of the phytodepuration process employing a porous medium, showed to present important health advantages. In particular, there were no significant odor emissions and there was no evidence of the proliferation of insects and other disease vectors. The plant achieved the following mean abatement efficiencies: 92.9% for BOD5, 79.2% for COD and 94% for Suspended Solids. With regard to fecal indicators average efficiencies of 98.8% for fecal coliforms and 97.9% for fecal enterococci were achieved. The UASB reactor showed an important role in achieving this result. The research was also aimed at evaluating the optimal operating conditions for the UASB reactor in terms of hydraulic load and organic volumetric loading. The achieved results hence indicated that the process may be highly effective for small rural communities in tropical and sub-tropical areas.
    • UAV Based Spatiotemporal Analysis of the 2019–2020 New South Wales Bushfires

      Fahim Ullah; Sara Imran Khan; Hafiz Suliman Munawar; Zakria Qadir; Siddra Qayyum (MDPI AG, 2021-09-01)
      Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.
    • UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction

      Honghai Zhang; Yongjie Yan; Shan Li; Yuxin Hu; Hao Liu (MDPI AG, 2021-11-01)
      Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.
    • UAV Photogrammetry Surveying for Sustainable Conservation: The Case of Mondújar Castle (Granada, Spain)

      Antonio Orihuela; María Aurora Molina-Fajardo (MDPI AG, 2021-12-01)
      Mondújar Castle is an Andalusi fortress located in the Valle de Lecrín (Granada, Spain). It had strategic importance in the final years of the Kingdom of Granada. The king Muley Hacén lived there before passing away, resulting in the popularisation of Romantic legends around its construction. Despite these folktales, the fortress has never been surveyed or restored and a complete architectural graphic study of this place is lacking. Therefore, it is essential to document the architectural heritage to collect relevant information for conservation work. Our main goal is to better understand the origin, architectural influences and building phases of the fortress, which requires historical and surveying methods. We present a historical approximation, followed by a photogrammetric survey. This is the first study on the medieval fortress and its subsequent Castilian refortification (executed around 1500). We conclude that it is not plausible that this place was the location of any legendary palaces. Apart from its historical and constructive significance, the use of Islamic funerary elements, probably coming from the Royal Nasrid Cemetery, makes this castle unique. Therefore, the preservation and understanding of this monument should be a priority within the sustainable development of the region.
    • UAV-Based Bridge Inspection via Transfer Learning

      Mostafa Aliyari; Enrique Lopez Droguett; Yonas Zewdu Ayele (MDPI AG, 2021-10-01)
      As bridge inspection becomes more advanced and more ubiquitous, artificial intelligence (AI) techniques, such as machine and deep learning, could offer suitable solutions to the nation’s problems of overdue bridge inspections. AI coupling with various data that can be captured by unmanned aerial vehicles (UAVs) enables fully automated bridge inspections. The key to the success of automated bridge inspection is a model capable of detecting failures from UAV data like images and films. In this context, this paper investigates the performances of state-of-the-art convolutional neural networks (CNNs) through transfer learning for crack detection in UAV-based bridge inspection. The performance of different CNN models is evaluated via UAV-based inspection of Skodsberg Bridge, located in eastern Norway. The low-level features are extracted in the last layers of the CNN models and these layers are trained using 19,023 crack and non-crack images. There is always a trade-off between the number of trainable parameters that CNN models need to learn for each specific task and the number of non-trainable parameters that come from transfer learning. Therefore, selecting the optimized amount of transfer learning is a challenging task and, as there is not enough research in this area, it will be studied in this paper. Moreover, UAV-based bridge inception images require specific attention to establish a suitable dataset as the input of CNN models that are trained on homogenous images. However, in the real implementation of CNN models in UAV-based bridge inspection images, there are always heterogeneities and noises, such as natural and artificial effects like different luminosities, spatial positions, and colors of the elements in an image. In this study, the effects of such heterogeneities on the performance of CNN models via transfer learning are examined. The results demonstrate that with a simplified image cropping technique and with minimum effort to preprocess images, CNN models can identify crack elements from non-crack elements with 81% accuracy. Moreover, the results show that heterogeneities inherent in UAV-based bridge inspection data significantly affect the performance of CNN models with an average 32.6% decrease of accuracy of the CNN models. It is also found that deeper CNN models do not provide higher accuracy compared to the shallower CNN models when the number of images for adoption to a specific task, in this case crack detection, is not large enough; in this study, 19,023 images and shallower models outperform the deeper models.
    • UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

      Hafiz Suliman Munawar; Fahim Ullah; Siddra Qayyum; Sara Imran Khan; Mohammad Mojtahedi (MDPI AG, 2021-07-01)
      Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.
    • Ubim Fiber (<i>Geonoma baculífera</i>): A Less Known Brazilian Amazon Natural Fiber for Engineering Applications

      Belayne Zanini Marchi; Michelle Souza Oliveira; Wendell Bruno Almeida Bezerra; Talita Gama de Sousa; Verônica Scarpini Candido; Alisson Clay Rios da Silva; Sergio Neves Monteiro (MDPI AG, 2022-12-01)
      The production of synthetic materials generally uses non-renewable forms of energy, which are highly polluting. This is driving the search for natural materials that offer properties similar to synthetic ones. In particular, the use of natural lignocellulosic fibers (NLFs) has been investigated since the end of 20th century, and is emerging strongly as an alternative to replace synthetic components and reinforce composite materials for engineering applications. NLFs stand out in general as they are biodegradable, non-polluting, have comparatively less CO<sub>2</sub> emission and are more economically viable. Furthermore, they are lighter and cheaper than synthetic fibers, and are a possible replacement as composite reinforcement with similar mechanical properties. In the present work, a less known NLF from the Amazon region, the ubim fiber (<i>Geonoma bacculifera</i>), was for the first time physically characterized by X-ray diffraction (XRD). Fiber density was statistically analyzed by the Weibull method. Using both the geometric method and the Archimedes’ technique, it was found that ubim fiber has one of the lowest densities, 0.70–0.73 g/cm<sup>3</sup>, for NLFs already reported in the literature. Excluding the porosity, however, the absolute density measured by pycnometry was relatively higher. In addition, the crystallinity index, of 83%, microfibril angle, of 7.42–7.49°, and ubim fiber microstructure of lumen and channel pores were also characterized by scanning electron microscopy. These preliminary results indicate a promising application of ubim fiber as eco-friendly reinforcement of civil construction composite material.
    • Ubiquitous Occurrence of a Biogenic Sulfonate in Marine Environment

      Xiaofeng Chen; Yu Han; Quanrui Chen; Huaying Lin; Shanshan Lin; Deli Wang; Kai Tang (MDPI AG, 2022-01-01)
      The biogenic sulfonate 2,3-dihydroxypropane-1-sulfonate (DHPS) is a vital metabolic currency between phytoplankton and bacteria in marine environments. However, the occurrence and quantification of DHPS in the marine environment has not been well-characterized. In this study, we used targeted metabolomics to determine the concentration of DHPS in the Pearl River Estuary, an in situ costal mesocosm ecosystem and a hydrothermal system off Kueishantao Island. The results suggested that DHPS occurred ubiquitously in the marine environment, even in shallow-sea hydrothermal systems, at a level comparable to that of dimethylsulfoniopropionate. The concentration of DHPS was closely related to phytoplankton community composition and was especially associated with the abundance of diatoms. Epsilonproteobacteria were considered as the most likely producers of DHPS in shallow-sea hydrothermal systems. This work expands current knowledge on sulfonates and presents a new viewpoint on the sulfur cycle in hydrothermal systems.
    • UGC Sharing Motives and Their Effects on UGC Sharing Intention from Quantitative and Qualitative Perspectives: Focusing on Content Creators in South Korea

      Do-Hyung Park; Sungwook Lee (MDPI AG, 2021-08-01)
      Recently, user-generated content (UGC) has been in the limelight. This study investigates why Internet users share their own UGC and reveals how the motives behind UGC sharing affect UGC sharing intentions both quantitatively and qualitatively. Based on motivations established in existing online communication literature, theoretical UGC motives are identified. Using online surveys administered to 300 users in South Korea, factor analysis is performed to identify empirical UGC sharing motives, and regression analyses shows how UGC sharing motives affect UGC sharing intention in terms of quality and quantity. A total of 10 theoretical UGC motives are consequently factorized into five motives. It is revealed that three motives—self-creation, self-expression, and reward—are related to individual purposes. Users get enjoyment from creating content, they want to be recognized by others, and further expect to be rewarded socially and economically. The other two motives, community commitment and social relationships, are related to social purposes. Users share UGC as a means of communication, desire feedback from others, and want to feel a sense of belonging within certain communities. All of these motives positively affect UGC sharing intention. This is the first study to empirically clarify UGC sharing motives. In addition, this study reveals UGC-centric self-creation and self-expression motives, which have not been the focus of previous online communication studies. Finally, the research results suggest how UGC site managers can adopt practical strategies related to UGC management.
    • UK Consumers’ Preferences for Ethical Attributes of Floating Rice: Implications for Environmentally Friendly Agriculture in Vietnam

      Vo Hong Tu; Steven W. Kopp; Nguyen Thuy Trang; Andreas Kontoleon; Mitsuyasu Yabe (MDPI AG, 2021-07-01)
      Vietnam plays an important role in bearing global food security. However, Vietnamese rice farmers face several challenges, including pressures to develop sustainable livelihoods while reducing the environmental impacts of their production activities. Various Vietnamese agricultural restructuring policies were promulgated to promote the adoption of environmentally friendly practices to generate high value added for rice farmers, but the farmers are reluctant to adopt them because of perceived lack of demand. Decreasing consumption of rice in Asia and increasing demands in Europe shaped Vietnamese rice exporting policies. New trade agreements, such as the UK–Vietnam Free Trade Agreement, offer new target markets for Vietnamese rice farmers. This research provides empirical evidence related to the preferences of UK consumers for ethical attributes for floating rice imported from Vietnam. Floating rice represents a traditional method of rice cultivation that relies on the natural flooding cycle. Its cultivation uses very few agrochemical inputs and provides several other environmental, economic, and social benefits. In an online survey, the study used a choice experiment that asked 306 UK consumers to report their preferences for one kilo of floating rice with three non-market attributes: reduction in carbon dioxide emissions, allocation of profits to the farmers, and restitution of biodiversity. Overall, study participants favored the attributes of floating rice, but reported utility for only the “fair trade” attribute and for a marginal willingness to pay premiums for profit allocations to farmers. Consumers did not find value in either CO<sub>2</sub> emission reduction or biodiversity improvement. Results from the study provide recommendations to develop agricultural programs, distribution strategies, and informational methods to encourage floating rice consumption in the UK.
    • UK Government Policy and the Transition to a Circular Nutrient Economy

      Andy Yuille; Shane Rothwell; Lynsay Blake; Kirsty J. Forber; Rachel Marshall; Richard Rhodes; Claire Waterton; Paul J. A. Withers (MDPI AG, 2022-03-01)
      The “circular economy” is an increasingly influential concept linking economic and environmental policy to enable sustainable use of resources. A crucial although often overlooked element of this concept is a circular nutrient economy, which is an economy that achieves the minimization of nutrient losses during the production, processing, distribution, and consumption of food and other products, as well as the comprehensive recovery of nutrients from organic residuals at each of these stages for reuse in agricultural production. There are multiple interconnecting barriers to transitioning from the current linear economic system to a more circular one, requiring strongly directional government policy. This paper uses interpretive policy analysis to review six UK government strategies to assess their strengths and weaknesses in embracing nutrient circularisation. Our analysis highlights the acute underrepresentation of the circular nutrient economy concept in these strategies as well as the potential to reorient the current policy towards its development. We find significant barriers to transition presented by ambiguity in key policy terms and proposals, the use of inappropriate indicators, the lack of a systematic approach to key sustainability objectives, and the presence of a “techno-optimist imaginary” throughout the strategies. We develop these findings to make recommendations to help integrate definitions, objectives, and activities across the policy domains necessary for the operational development of a circular nutrient economy.
    • Ukrainian Migrants in Poland and the Role of an Employer as the Channel of Information during the COVID-19 Pandemic

      Anita Adamczyk; Monika Trojanowska-Strzęboszewska; Dorota Kowalewska; Robert Bartłomiejski (MDPI AG, 2022-04-01)
      This paper examines communication processes between state institutions and migrants under the conditions of the COVID-19 pandemic. It aims to determine where migrants obtain their information on specific legal regulations and restrictions on rules of conduct in the public space and professional environment. This issue is examined through the example of Ukrainian labour immigration in Poland. Referring to the results of our survey research, it is established that in a crisis, when the importance of information in the public sphere increases and, at the same time, direct social contacts are restricted, the special role of the employer is revealed. The employer is perceived not only as an entity offering work, but also as an important channel of information about state policy, regulations and rules of conduct applicable in a crisis. These findings are an indication, on the one hand, for state institutions to take this role of employers into account in migration policy and, on the other hand, for employers themselves to be aware of their social role towards migrants and play it responsibly. We believe that the study, conducted in the first two months of the pandemic, has become very timely with the outbreak of the Ukrainian–Russian war.