Quality Labels Research Articles

Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.

High quality data labeling is essential for improving the accuracy of deep learning applications in medical imaging. However, noisy images are not only under-represented in training datasets, but also, labeling of noisy data is low quality. Unfortunately, noisy images with poor quality labels are exacerbated by traditional data augmentation strategies. Real world images contain noise and can lead to unexpected drops in algorithm performance. In this paper, we present a non-traditional, purposeful data augmentation method to specifically transfer high quality automated labels into noisy image regions for incorporation into the training dataset. The overall approach is based on the use of paired images of the same cells in which variable image noise results in cell segmentation failures. Iteratively updating the cell segmentation model with accurate labels of noisy image areas resulted in an improvement in Dice coefficient from 77% to 86%. This was achieved by adding only 3.4% more cells to the training dataset, showing that local label transfer through graph matching is an effective augmentation strategy to improve segmentation.

The Role of Disclosure in Green Finance

We study the design features of disclosure regulations that seek to trigger the transition of the global economy and ask whether such regulatory interventions are likely to bring about sufficient market discipline to achieve socially optimal climate targets. We categorize the transparency obligations stipulated in finance regulation as either compelling the standardized disclosure of raw data, or providing quality labels that signal desirable characteristics of investment products based on a uniform methodology. Both categories of transparency requirements canbe imposed at activity, issuer, and portfolio level. Finance theory and empirical evidence suggest that investors may prefer green over dirty assets for both financial and non-financial reasons and may thus demand higher returns from environmentally-harmful investment opportunities. However, the market discipline that this negative cost of capital effect exerts on dirty issuers is potentially attenuated by countervailing investor interests and does not automatically lead to socially optimal outcomes. Mandatory disclosure obligations and their (public) enforcement can play an important role in finance strategies. They prevent an underproduction of the standardized high-quality information that investors need in order to allocate capital according to their preferences. However, the rationale behind regulatory intervention is not equally strong for all categories and all levels of green disclosure obligations. Corporate governance problems and other agency conflicts in intermediated investment chains do not represent a categorical impediment for finance strategies. However, the many forces that may prevent markets from achieving socially optimal equilibria render disclosure-centered finance legislation a second best to more direct forms of regulatory intervention like global carbon taxation and emissions trading schemes. Inherently transnational market-based finance concepts can play a supporting role in sustainable transition, which is particularly important as long as first-best solutions remain politically unavailable.

Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction from remote sensing images. CNN-based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset, which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this article first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different upsamplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.

Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification

For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability.

Food Labels (Quality, Origin, and Sustainability): The Experience of Czech Producers

Food labels allow producers to promote the unique selling points of their products and help consumers buy products with value-adding qualities. The aim of the present article is hence to explore Czech food producers’ expectations and experience with selected food labels. The selection was narrowed down to enterprises that had succeeded in the certification processes within the food labeling systems of European quality and origin labels (protected designation of origin (PDO), protected geographical indication (PGI), and traditional specialty guaranteed (TSG)), as well as schemes that fall under the remit of the Ministry of Agriculture of the Czech Republic, such as Klasa (Česká potravina/Czech food, Regionální potravina/regional food, and BIO). The research was made using an online questionnaire in January–February 2018, and was focused on determining possible discrepancies in expectations that the companies had before applying for quality/origin labels and their actual experience with the labelling systems, the benefits they had gained, and their overall experience with the labels. To compare the respondents’ expectations and their actual experience, both the non-parametric Wilcoxon test and t-test were used. The results show that the respondents positively evaluated the effects of marketing communication, but their expectations had been higher than what they experienced.

Adopting Environmentally Friendly Farming Practices and the Role of Quality Labels and Producer Organisations: A Qualitative Analysis Based on Two European Case Studies

Various drivers behind the adoption of environmentally friendly practices have been investigated at the farm level in the literature, e.g., farmers’ motivations and attitudes, farms’ structure, and management or policies. Yet, the way in which quality labels and producer organisations influence the adoption of environmentally friendly practices by farmers is still under-researched. We contribute to this topic and present the results of qualitative interviews with producer organisations, conducted in 2019 in two contrasting case studies: the pig sector in Brittany (western France), and the olive oil sector in Crete (Greece). Our study shows that economic actors of food supply chains in these two case studies use European quality labels, a couple of national schemes, and a proliferation of private quality labels (in Brittany’s pig sector). Our interviews reveal that many quality labels, for which agricultural farming systems must comply with a set of rules, are not specifically aimed at improving environmental impacts. In the Cretan olive oil sector, we observe several European public labels. In the French pig sector, many quality labels do not include requirements for practices aiming at improving the environment, but instead focus on other practices that matter for society, namely improving animal welfare. However, advisory services provided by the producer organisations can play a key role in the adoption of environmentally friendly practices. They include research programmes and agronomic events. In Crete, producer organisations are able to offer technical assistance thanks to European support programmes.

The Analysis of Risks to Personal Data Security

Article 32 of the EU General Data Protection Regulation imposes the obligation to implement appropriate safeguards to protect personal data. It states that the application of adequate measures is to be preceded by a risk analysis and evaluation. In the current paper, as the main risk factors, probability and consequences were assumed that take into account the basic attributes of information, i.e. confidentiality, integrity and availability. Next, a risk analysis methodology based on the risk matrix is proposed. The issue discussed in the publication is currently valid and still requires careful analysis in order to develop universal standards aimed at establishing certification mechanisms as well as quality labels and markings in terms of personal data protection.

Do Food Quality Schemes and Net Price Premiums Go Together?

Abstract This article addresses the issue of the profitability of Food Quality Scheme (FQS) products as compared to reference products, which are defined as analogous products without quality label. We approach this question by taking into account the level of the value chain (upstream, processing, and downstream), the sector (vegetal, animal, seafood) and the type of FQS (PGI, PDO, Organic). We collected original data for several products produced in selected European countries, as well as in Thailand and Vietnam. Comparisons depending on value chain level, sector and FQS are possible by using two comparable indicators: price premium and net price premium (including cost differential). The following principal conclusions were reached: 1) Price is higher for FQS products than for the reference products, regardless of the production level, the type of FQS or the sector; 2) Price premiums generated by FQS do not differ along the value chain, nor between sectors (vegetal, animal or seafood/fish); 3) Price premium for organic products is significantly higher than for PGI products, and this conclusion holds at upstream and processing levels, taking into account the costs directly related to production; 4) All organic products and almost all PDO and PGI products analysed benefit from a positive quality rent; 5) At upstream level and processing level, the relative weight of intermediate consumption in the cost structure is lower for organic products than for reference products.

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

Spatial and temporal diversity of service plant management strategies across vineyards in the south of France. Analysis through the Coverage Index

‘Service plants’ include spontaneous vegetation or sown species of cover crops associated with perennial crops in the rows or inter-rows with a high potential to provide ecosystem functions and services. In vineyards, service plants target specific services depending on the management strategy implemented by the winegrower, including the plant species, the surface covered, the plant growth control and destruction date. Understanding the management strategies linked to their associated target services at the regional scale is necessary to better help winegrowers, advisers and policy makers regarding an adapted use of service plants. To do this, we conducted a survey in 2016 among 334 winegrowers in Languedoc-Roussillon region in France, enquiring about their service plant management practices during the season 2014–2015. Given the diversity of the strategies of service plant management, we proposed a typology analyzing their spatial and temporal dimensions. Further, we present a Coverage Index (CI), which combines both temporal and spatial dimensions of the service plant management strategies. We conducted a multiple components analysis and clustering to create a vineyard typology and applied linear models to find correlations between the CI and specific vineyard characteristics. Three quarters of interviewed winegrowers sowed or maintained service plants in their vineyards; 41 % used a winter service plant strategy; 8.4 % a semi-permanent and 27.3 % a permanent service plant strategy. The preferred surface coverage strategy was full surface during grapevine dormancy and its reduction to half of the inter-rows after grapevine budburst. However, the diversity of surface coverage strategies during the grapevine vegetative period was remarkable. Lower water resources and specific soil characteristics were not linked to the service plant management strategies. Higher CI was associated with vineyards presenting quality labels (PDO and Organic), independent winemaking and lower target yields, showing that the added value of producing high quality wine plays an important role when implementing service plants in vineyards. Overall, our study showed: i) the popularity of spontaneous service plant strategies; ii) the spatial and temporal diversity of service plant management strategies and iii) the utility of the CI to study the implementation of service plants and to understand the motivations and constraints of their use.

Price Fluctuation, Protected Geographical Indications and Employment in the Spanish Small Ruminant Sector during the COVID-19 Crisis.

Simple SummaryThis work assesses milk and meat price fluctuations from small ruminants during the coronavirus disease (COVID-19) crisis in Spain, using official data provided by the Interprofessional Dairy Organization (InLaC) and a representative sample of agricultural markets fixing prices per kilogram of lamb and goat kid meat. Data showed a drop in meat and goat milk prices during the period of confinement, which is only maintained for goat milk over the three following months. Similarly, analysis of data from the most important production areas, involving three protected geographical indications (PGI) of lamb meat, suggested that this quality label had a beneficial effect on sales and price stability in times of crisis. On the other hand, despite the impact of the COVID-19 pandemic on the Spanish labor market, the small ruminant sector did not seem to be affected in terms of the number of workers when comparing the period before and after the pandemic. All these factors are of interest for producers when making decisions regarding the management of flocks and adapting their marketing strategies for a down market or unexpected volatile circumstances.Official milk prices in the Spanish small ruminant sector were used for 5 years (2015–2019) to analyze the effect caused by the coronavirus disease (COVID-19) crisis in 2020. Meat price fluctuations were also studied using the weekly prices officially provided by some of the main agrarian markets of the country (n = 6) in 2019 and 2020. Moreover, the sales and prices of three protected geographical indications (PGI) of lamb meat served to study the marketability when the products are sold or not under these quality labels in a crisis context. According to Spanish Government’s official communications, 2020 was divided in three periods of study (pre-COVID-19, total confinement and post-confinement). The evolution of employment in this subsector in 2020, as a direct consequence of this crisis, was also analyzed considering data provided by producers. Results showed an intra-annual seasonal effect for milk prices in 2020 for both livestock species, as observed in previous years. However, a negative economic impact on goat milk prices due to the pandemic was checked during the confinement and post-confinement months. Sheep milk prices remained stable. Lamb and goat kid meat prices showed a similar trend in comparison with 2019 during the pre-COVID-19 period. The total confinement period recorded a short interval of 1–2 weeks in which the prices declined, before the suspension of quotations in many markets. In contrast, once confinement was completed, meat prices for both ruminant species rapidly reached levels that existed before the coronavirus crisis. Overall data suggested the protective effect of the PGI marks on lamb meat. Lambs with a PGI had better 2020 prices than non-PGI lambs (+8%), regardless of the period analyzed. Moreover, with fewer lambs sold in 2020, there was a relevant drop in sales of non-PGI lambs vs. PGI (−19% vs. −2%) during the first 7 months. Finally, there was little or no readjustment of the workforce in the small ruminant flocks.

CLUSTERS, EUROPEAN POLICY IN EXCELLENCE OF MANAGEMENT

The European Union (EU) plays an important role in the developing of clusters, defined by the European Commission as groups of specialized enterprises – often SMEs – and other related supporting actors that cooperate closely together in a particular location. As a result, the EU launched the pan-European initiative to support cluster management (European Cluster Excellence Initiative). It recognizes the performance of cluster management by quality labels such as the Bronze, Silver, and Gold issued by The European Secretariat of Cluster Analysis. With reference to these certifications, the authors analyzed the cluster management excellence by critically studying the labels granted in terms of trends, numbers, sectors, countries, and regions. Based on the gathered results, the clusters initiatives (new or already existed) can benchmark themselves. Regional, national, and European policymakers will be able to estimate how specific factors of political, geographical, demographic, access to raw materials, and level of development can influence the number of clusters, their quality of management, and cluster typology. The goal of the research is to identify the number and type (bronze, silver, and gold label) of clusters in the EU and UK. As research methods, analyzes were performed using the European Cluster Collaboration Platform (ECCP) and European Secretariat for Cluster Analysis (ESCA) data. The main results of the research show that clusters differ not only in size or activity but also in quality. Most clusters are found in the rich countries of Western Europe. Most clusters in Europe that have a bronze label are often located on the Iberian Peninsula, the Balkans, and Central Europe.

Exploring the motives behind the purchase of western imported food products. A phenomenological study from a Muslim-dominated region

PurposeThe usage and preference of western imported food in a Muslim-majority state signifies its importance and relevance in a specific culture. However, the inclination and preference toward imported food products must be backed by a strong motivation, when the religion of Islam does not permit overspending yet the amount spent on such imported food products is overwhelming. Hence, the purpose of this study is to explore the motivation behind this behavior.Design/methodology/approachThis is a qualitative study, and in-depth semi-structured interviews were conducted with 90 participants comprising professionals, housewives and university students from eight cities in Pakistan, which represented different regions and demographic variables. These were Karachi, Lahore, Islamabad, Peshawar, Quetta, Hyderabad, Faisalabad and Larkana. The technique used to analyze the qualitative interview findings was thematic content analysis. To confirm the results, Leximancer software Version 4.5 was used to reanalyze and validate them. Moreover, the purposive sampling method has been used in this research.FindingsThe findings from the qualitative-focused interviews revealed that the product attributes of packaging with attractive colors, design, size, overall quality material, taste and labeling with maximum product information influenced their purchase behavior. The vast majority reported that food products coming from the west needed to be halal, and this is an important deciding factor for purchase. Also, the level of religiosity related to western imported food buying behavior varies from city to city, which itself an interesting finding from a Muslim-majority population. Brand trust, loyalty, satisfaction, subjective norms were influential factors for Muslim consumers’ purchase behavior.Research limitations/implicationsThis paper is qualitative in nature, and therefore, the generalizability of the study results is limited. Also, this study only focused on Muslim consumer buying behavior from a Muslim-dominated country.Practical implicationsThis study is instrumental for western food producers and exporters, providing valuable information about the motives behind the purchase of western imported food products in Pakistan, and by extension, potentially in Muslim countries in general. The study’s findings would add value to the field of consumer behavior, in which little research has been conducted on the relationship between consumer motives in context with Muslims’ consumer behavior toward western imported food products.Social implicationsThe presence of western imported food products may give better options for consumers so that they can pick a quality product for their own and family usage. The placement of the halal logo and extra care of halal ingredients also assures the religious and cultural requirements, enabling the western imported food products to penetrate quickly.Originality/valueThe findings of the qualitative-focused interviews revealed that the level of religiosity varies from city to city. Even though the core religion is Islam, the level of religious commitment varies in different cities when it comes to the purchase of western imported food products. The interview findings discovered some reasons behind this behavior such as consumer demographic profile, cultural background, income level, education, lifestyle, family background and social class. This means that demographic variation plays an important role in religious commitment and especially across cites that possess different cultural and behavioral patterns.

Effects of different emulsifier substitutes on artisanal ice cream quality

This work aims at assessing the performances of eight commercial emulsifier substitutes (not identified as additives) in an artisanal ice cream formulation, in comparison with the common mono- and di-glycerides of fatty acids (MDG). Besides the quality features of mixes (density, soluble solids, rheological properties, freezing point depression) and ice creams (extrusion parameters, overrun, firmness, melting behavior, shape retention), also the emulsifying activity index (EAI) and creaming stability (CS) of the considered ingredients were tested. No significant correlations were found between EAI or CS and quality characteristics of ice creams, confirming the singular role of MDG in ice cream structuring. Citrus fibers excessively increased the mix apparent viscosity compared to the reference sample containing MDG (435 ± 3 vs 89 ± 1 mPa s). Lupine, pea, and rice proteins, as well as citrus fibers significantly improved ice cream melting behavior (1.60 ± 0.12 – 2.33 ± 0.01 g/min melting rate vs. 2.62 ± 0.05 g/min of the reference sample). However, at the tested doses there was not a single ingredient as effective as MDG on ice cream properties. A combination of different substitutes in different amounts can be a valuable strategy in order to effectively replace MDG and attain the goal of an ice cream with good quality and clean label.

Oleotourism: A Comparison of Three Mediterranean Countries

The recent trends in the tourism industry, as well as the attention paid by scholars, practitioners, and institutions, show the relevance of natural tourism as a lever for local development (United Nations World Tourism Organization -UNWTO-, 2005). In this scenario, the business of oleotourism, namely, a form of domestic tourism based on activities related to olive oil production, its tasting, and some connected rural experiences such as harvesting, is continuously—albeit slowly—growing in Europe and especially in the Mediterranean area. Scholars agree that oleotourism plays a key role for multiple reasons, due to its relations to local territories, firms, resources, and other forms of tourism, also favoring sustainable development. Due to the interplay with the local context, the authors plan to combine and compare the evidence from three Mediterranean countries offering examples of tourism initiatives based on olive oil, namely, Spain, Italy, and Croatia. The similarities and differences emerging from the comparison will expand the understanding of this phenomenon and lead to the highlighting of key features and choices in favoring its development in the coming years. Therefore, this research, through the analysis of both theoretical and practical evidences and data from the local contexts, aims to identify additional knowledge for scholars, managers, and policy-makers. The results of the analysis allow the authors to conclude that, although each of the three countries is in a different stage of development, they all have elements that seem to be common to this type of tourism. Finally, it is concluded that oleotourism can be characterized as a form of sustainable tourism, given the preservation of local customs and landscapes, the participation of the resident community, or the development of quality and food safety labels.

EU quality label vs organic food products: A multigroup structural equation modeling to assess consumers’ intention to buy in light of sustainable motives

Consumers are increasingly paying attention to their food choices in light of sustainability. Regional and locally produced food, as well as organic products, are experiencing an increasing success amongst consumers as perceived as authentic high-quality food products, able to contribute to sustainable methods of production and consumption. Applying the Theory of Reasoned Action, this work compares consumers’ intention to buy EU quality label and organic food products. A multi-group structural equation modeling was used to estimate differences between perceived product quality, sustainable motives (i.e. willingness to pay a premium price for sustainable products and products with recyclable packaging), and health concern while controlling for product knowledge. A total of 155 organic shoppers and 215 EU quality label shoppers were surveyed for this purpose. Results revealed different motives at the roots of the two products buying choices. On one hand, shoppers are willing to pay a premium price for sustainable EU quality label foods. On the other hand, recyclable packaging is mandatory to shape the intention to buy organic foods. The study proposes theoretical and managerial implications and draws further research studies on consumers’ food choices.

Impact assessment of water and nutrient reuse in hydroponic systems using Bayesian Belief Networks

Abstract Water-saving agricultural practices can reduce negative environmental impacts in water-scarce regions all over the world. This study deals with an innovation that combines hydroponic crop production and municipal wastewater reuse for irrigation purposes. The research question was what impacts such hydroponic water reuse systems have on product confidence, economic viability, groundwater recharge, biodiversity and landscape quality. It should also be clarified under which conditions and with which measures these systems can be sustainable. To answer these questions, a number of generic hydroponic water reuse systems were modeled and assessed using a Bayesian Belief Network that included both numerical values and expert knowledge. The hydroponic water reuse systems with the most positive overall impacts are small-scale food production systems (tomatoes) equipped with lighting and heating whose products are marked with a quality label or with a label for regional products. The systems are located in a former industrial area. In addition, a wetland system and landscape integration are implemented as landscaping measures. Hydroponic systems can be operated economically viable, their products have a high level of product confidence and their ecological impacts can be positive. No tradeoffs have to be accepted between economic, social and ecological goals.

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest multiply-annotated volumetric medical imaging data set reported. To annotate this data set, we developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports with an average F-score of 0.976 (min 0.941, max 1.0). We also developed a model for multi-organ, multi-disease classification of chest CT volumes that uses a deep convolutional neural network (CNN). This model reached a classification performance of AUROC >0.90 for 18 abnormalities, with an average AUROC of 0.773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data. We show that training on more labels improves performance significantly: for a subset of 9 labels - nodule, opacity, atelectasis, pleural effusion, consolidation, mass, pericardial effusion, cardiomegaly, and pneumothorax - the model's average AUROC increased by 10% when the number of training labels was increased from 9 to all 83. All code for volume preprocessing, automated label extraction, and the volume abnormality prediction model is publicly available. The 36,316 CT volumes and labels will also be made publicly available pending institutional approval.

Storytelling in applications for the EU quality schemes for agricultural products and foodstuffs: place, origin and tradition

Aim of study: How are successful applications for quality labels for food and agricultural products written? This research aims to answer this question through the study of the applications for three quality labels within the EU Scheme for Agricultural and Foodstuffs: Protected Designation of Origin (PDO), Protected Geographical Indication (PGI) and Traditional Speciality Guaranteed (TSG). The research focuses in determining the topics and narratives that describe the link with the geographical area for the two former and the traditional character for the latter.Area of study: Europe.Material and methods: Using a Qualitative Data Analysis, the research analyses 132 products registered on the scheme between January 2016 and April 2019. The researcher categorized a total of 1,724 excerpts and studied the sections on the link with the geographical area for PDOs and PGIs, and the key elements establishing the product’s traditional character for TSGs.Main results: Main results offer quantitative and qualitative outputs. They indicate that cultural and social issues make up the half of the story arguing about the products, and natural, space/place and time/history make up the second half in proportions that depend on the type of scheme. Topic and storytelling analyses revealed particular understandings of place, origins and traditions in narratives developing on agriculture, history and knowledge transference between generations. The applicants used socio-economic particularities, regional history, and environmental uniqueness to justify the special character of products.Research highlights: The results give practitioners, policymakers and institutions guidelines and recommendations about how to structure and write their applications for quality labels for food and agricultural products.

Genetic Characterization of the Local Pirenaica Cattle for Parentage and Traceability Purposes.

Simple SummaryDomestic livestock diversity is an important component of global biodiversity and molecular data have become essential for the characterization of genetic diversity in cattle. The aim of this study was to assess the effectiveness of a 30-short tandem repeat (STR) panel and reveal the genetic structure of a local Pirenaica breed compared with other breeds (Terreña, Blonde d’Aquitaine, Limousin, Salers and Holstein-Friesian) typically raised in the same geographic Basque region. The proposed STR panel could be used as an appropriate genetic tool to trace Pirenaica animals and their Protected Geographic Indication (PGI) products.Pirenaica is the most important autochthonous cattle breed within the Protected Geographic Indication (PGI) beef quality label in the Basque region, in northern Spain. The short tandem repeats (STRs) are powerful markers to elucidate forensic cases and traceability across the agri-food sector. The main objective of the present work was to study the phylogenetic relationships of Pirenaica cattle and other breeds typically raised in the region and provide the minimum number of STR markers for parentage and traceability purposes. The 30-STR panel recommended by the International Society of Animal Genetics-Food and Agriculture Organization of the United Nations (ISAG-FAO) was compared against other commercial STR panels. The 30-STR panel showed a combined matching probability of 1.89 × 10−25 and a power of exclusion for duos of 0.99998. However, commercial STR panels showed a limited efficiency for a reliable parentage analysis in Pirenaica, and at least a 21-STR panel is needed to reach a power of exclusion of 0.9999. Machine-learning analysis also demonstrated a 95% accuracy in assignments selecting the markers with the highest FST in Pirenaica individuals. Overall, the present study shows the genetic characterization of Pirenaica and its phylogeny compared with other breeds typically raised in the Basque region. Finally, a 21-STR panel with the highest FST markers is proposed for a confident parentage analysis and high traceability.