The Farm-Community Nexus: Metrics for Social, Economic, and Environmental Sustainability of Agritourism and Direct Farm Sales in Vermont

Viable working landscapes, vibrant communities, and healthy ecosystems are the building blocks of sustainable food systems. Small and medium farms are connective tissue, creating a system that is greater than the sum of its parts by linking consumers to producers and promoting environmental stewardship. Our approach considers sustainability through connections between farms, their communities, and visitors within an agritourism framework, including on-farm experiences, direct sales of agricultural products, and farmer-consumer interactions at markets. The goal is to contribute to the understanding, operationalization, and integration of metrics built on the ideals that viable, sustainable, and resilient food systems must support social, economic and environmental goals. The approach presented in this white paper: 1. Applied a sustainability framework to identify metrics relevant for social, economic, and environmental dimensions across farm, household, community, and statewide scales. 2. Identified existing data sets and current data gaps. 3. Identified linkages and impacts between social, economic and environmental dimensions of sustainability across scales and different frameworks. 4. Considered sustainability applied to direct sales and agritourism, with particular emphasis on the social floor required to promote individual, farmer, and community well-being, while protecting the environment by respecting our planetary boundaries. We categorized priority metrics under primary sustainability dimensions: Environmental – Open Space, Farm Products, Stewardship, and the Vermont Brand Economic – Economic Impacts, Consumer Spending, Farm Profitability, Farm Labor, and Farmland Social – Cultural Ecosystem Services, Labor Opportunities and Conditions, Social and Informational Infrastructure, Sense of Community, Demographic and Cultural Diversity, Good Governance, and Health, Safety, and Wellbeing Based on our assessment of existing and needed metrics summarized in this white paper, key recommendations to the UVM-ARS Center include: 1. Catalyze and synergize efforts and resources in Vermont to holistically address sustainability. 2. Explore and identify ways the Vermont brand—an important component of the state’s social, ecological and economic identity and culture—supports sustainability. 3. Focus on informational and data needs that are central to understanding and ensuring sustainability in Vermont, including longitudinal producer and consumer surveys. 4. Support a deep convergence of social and natural sciences in addressing sustainability. The goal is to provide an essential foundation for future research that will place the UVM-ARS Center for Food Systems Research at the forefront of this critical transdisciplinary area

Dashboard Framework. A Tool for Threat Monitoring on the Example of Covid-19

The aim of the study is to create a dashboard framework to monitor the spread of the Covid-19 pandemic based on quantitative and qualitative data processing. The theoretical part propounds the basic assumptions underlying the concept of the dashboard framework. The paper presents the most important functions of the dashboard framework and examples of its adoption. The limitations related to the dashboard framework development are also indicated. As part of empirical research, an original model of the Dash-Cov framework was designed, enabling the acquisition and processing of quantitative and qualitative data on the spread of the SARS-CoV-2 virus. The developed model was pre-validated. Over 25,000 records and around 100,000 tweets were analyzed. The adopted research methods included statistical analysis and text analysis methods, in particular the sentiment analysis and the topic modeling

Big Data Analytics: A Survey

Internet-based programs and communication techniques have become widely used and respected in the IT industry recently. A persistent source of "big data," or data that is enormous in volume, diverse in type, and has a complicated multidimensional structure, is internet applications and communications. Today, several measures are routinely performed with no assurance that any of them will be helpful in understanding the phenomenon of interest in an era of automatic, large-scale data collection. Online transactions that involve buying, selling, or even investing are all examples of e-commerce. As a result, they generate data that has a complex structure and a high dimension. The usual data storage techniques cannot handle those enormous volumes of data. There is a lot of work being done to find ways to minimize the dimensionality of big data in order to provide analytics reports that are even more accurate and data visualizations that are more interesting. As a result, the purpose of this survey study is to give an overview of big data analytics along with related problems and issues that go beyond technology

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research

Data Quality Management in Corporate Practice

The 21st century is characterized by a rising quantity and importance of Data and Infor-mation. Companies utilize these in order to gain and maintain competitive advantages. Therefore, the Data and Information is required both in high quantity as well as quality. But while the amount of Data collected is steadily increasing, this does not necessarily mean the same is true for Data Quality. In order to assure high Data Quality, the concept of Data Quality Management (DQM) has been established, incorporating such elements as the assessment of Data Quality as well as its improvement. In order to discuss the issue of Data Quality Management, this paper pursues the following goals: (1) Systematic literature search for publications regarding Data Quality Management (Scientific contributions, Practice reports etc.) (2) Provision of a structured overview of the identified references and the research mate-rial (3) Analysis and evaluation of the scientific contributions with regards to methodology and theoretical foundation (4) Current expression of DQM in practice, differentiated by organization type and in-dustry (based upon the entire research material) as well as assessment of the situation (how well are the design recommendations based upon research results) (5) Summary of unresolved issues and challenges, based upon the research materia

Transformação digital: integração de ferramentas BI com CRM e dados de vendas

In recent years, technological advancements and the escalating volume of data generated have encouraged companies to adopt new and improved management procedures, supported by business software able to accommodate this new reality. Business Intelligence (BI) tools serve this purpose, with the goal of providing businesses with the ability to extract useful information from available data, enabling them to position themselves in the target market with a greater understanding of the challenges and what can be done to achieve better results. BI involves the collection, processing, cleansing, and storage of data, as well as the implementation of analytical tools. Following this, as part of a comprehensive Digital Transformation (DT) project, Amorim, in particular the Amorim Cork SGPS (AC-SGPS) business unit, has observed a constant increase in the data flow and volume, as well as a growing need to maximize data utility. Consequently, one of the Amorim’s primary objectives is to invest in BI tools that support and simplify data analysis. In this context, the primary objective of my internship project was to integrate data from a CRM tool and the currently implemented ERP system into PowerBI (PBI) in order to facilitate the analysis of existing data. The project was divided into two case studies - Customer Service departments and Commercial departments of two Amorim group companies - where the PBI was implemented independently. Each case began with an analysis of the department’s data structure, followed by the collection of initial requirements from stakeholders, and concluded with the development and implementation of the solution. Special attention was deposited on the continuous participation of stakeholders throughout the development process so that they could make optimal use of the BI tools after implementation. Subsequently, a survey was conducted with the end users in order to collect and analyze the results and inquire the added value to the covered companies. According to the members of the departments, the new access to information is clearly superior to the methods previously used, as it makes the information easier to locate and contributes to a more independent and productive method of working. In addition, the significance of implementing this type of tool for monitoring and correcting processes from a factual and quantifiable data perspective while supporting the decision-making process was emphasized. Consequently, it is anticipated that the project will contribute, on the one hand, to a reduction in costs, as a result of increased productivity and a faster and more effective decision-making process, and, on the other hand, to an increase in revenues and profits, as a result of increased customer retention and attraction, as well as greater user satisfaction and motivation. Together, these contributions are intrinsically linked to the success of AC-SGPS’s multi-departmental DT project’s business strategy and long-term objectives.Nos últimos anos, os avanços tecnológicos e a crescente quantidade de dados gerados levaram as empresas a adotar novos e melhores procedimentos de gestão, apoiados por software empresarial com capacidade de fazer frente a esta nova realidade. É aqui que entram as ferramentas de Business Intelligence (BI), com o objetivo de proporcionar às empresas a capacidade de extrair informação útil dos dados disponíveis, permitindo que se posicionem no mercado alvo com uma maior compreensão dos desafios e do que pode ser feito para alcançar melhores resultados. BI engloba métodos de recolha, tratamento, limpeza e armazenamento de dados e a implementação de ferramentas analíticas. Neste seguimento, como parte de um projeto abrangente de Transformação Digital (TD), a Amorim, e em particular a unidade de negócios Amorim Cork SGPS (AC-SGPS), tem notado um aumento constante no fluxo e volume de dados, associado a uma necessidade crescente de tirar o máximo partido dos mesmos. Consequentemente, um dos principais objetivos do grupo Amorim passa pelo investimento em ferramentas de BI que tornem a análise de dados mais fácil e intuitiva. Neste contexto, o foco do meu projeto de estágio passou pela integração de dados de uma ferramenta CRM e do sistema ERP, atualmente implementados, com Power BI (PBI), a fim de simplificar e melhorar a análise da informação existente. O projeto foi dividido em dois casos de estudo - departamentos de Serviço de Apoio ao Cliente e departamentos Comerciais de duas empresas do grupo Amorim -, onde a implementação do PBI foi realizada separadamente. Para cada caso, começou-se por realizar uma análise da estrutura de dados da empresa, tendo-se seguido uma recolha de requisitos iniciais dos stakeholders, culminando com o seu desenvolvimento e implementação. Durante todo o processo de desenvolvimento, foi dada prioridade à participação constante dos stakeholders para que, numa fase pós-implementação, pudessem fazer o melhor uso possível das ferramentas BI. Posteriormente foi realizado um inquérito aos utilizadores como forma de recolher e analisar os resultados e averiguar o valor acrescentado às empresas abrangidas. De um ponto de vista geral, segundo os membros dos departamentos, o novo acesso à informação é claramente superior aos métodos utilizados até então, tornando-a mais fácil de encontrar e consequentemente contribuindo para um método de trabalho mais independente e produtivo. Foi também salientada a importância da implementação deste tipo de ferramenta na monitorização e correção de processos a partir de uma perspetiva factual e quantificável dos dados, ao mesmo tempo que apoia o processo de tomada de decisão. Por conseguinte, prevê-se que o projeto venha a contribuir, por um lado para uma redução dos custos, resultante de uma maior produtividade e de um processo de tomada de decisão mais rápido e eficaz, e por outro lado para um aumento das receitas e dos lucros, como resultado de uma maior retenção e atracão de clientes, a par de uma maior satisfação e motivação dos utilizadores. Estas contribuições, no seu conjunto, estão intrinsecamente ligadas ao sucesso da estratégia empresarial e dos objetivos a longo prazo definidos no projeto multidepartamental de TD da AC-SGPS.Mestrado em Engenharia e Gestão Industria