This book on the fisheries sector in India, through primary surveys as well as secondary literature, brings out various nuances of the sector and its trade opportunities, the complexities surrounding the supply chain of fish, as well as the evolution of its marketing channels. A distinctive feature of this book is that it carries out a comprehensive mapping of the fisheries supply chain, by taking into account both marine and freshwater fish. It identifies various players, especially traders who take part in the product flow, irrespective of the impact each of them has on the value provided to the end customer. While members of the supply chain include all individuals or organisations between whom interaction takes place, directly or indirectly from the point of production to consumption, this study also distinguishes between primary and peripheral members to make a complex network more manageable. Moreover, the book provides a comprehensive analysis of the emerging marketing channels- both organised and unorganised- in this highly perishable food segment. It provides important insights into the current scenario, focusing on the emergence of newer forms of marketing such as multinationals and e-retailing, while highlighting how traditional forms such as ‘mom-and-pop’ shops have continued to sustain, despite the challenges they face. The findings from India are also compared to global experiences of other fish producing and exporting countries such as Bangladesh, Indonesia, and Thailand to offer a comparison of the differences and similarities in the supply chains of various countries. The book provides important takeaways for researchers and PhD scholars working in the area of fisheries as well as supply chains. Since this book is based on field visits to different parts of the country it brings out the ground realities along with interesting insights and important policy implications for the sector, and should, therefore, appeal to policymakers as well.

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Environmental Metagenomics, Water Quality and Suggested Remediation Measures of Polluted Waters: A Combined Approach is a reference handbook for scientists, engineers and early-career researchers seeking guidance in the areas of water quality, and remediation studies. The comprehensive book, which includes case studies and applications from a range of contributors in the field, offers an essential resource in the science of water quality assessment. - Includes a range of applications and case studies in wetland, riverine, drinking, and groundwater metagenomics, along with approaches for the remediation of pollutants from wastewater - Offers the latest updates on environmental metagenomics and its correlation with water environments, remediation measures, and SDGs - Provides key contributions from global researchers in the fields of water chemistry, environmental science, engineering, and public health

A key challenge in systematically collecting data on intermediary agri-food value chain actors is that value chains take the form of a network, with actors linked by a series of transactions. Moreover, we have limited ex ante knowledge about the structure or scale of these networks, which complicates the construction of valid sampling frames and limits traditional random sampling approaches to collect data. To address these challenges, we adapt the respondent-driven sampling approach to collect data on intermediary agri-food value chain actors within their transaction-linked network and implement this approach in the arabica coffee and soybean value chains in Uganda and the rice and potato value chains in Bangladesh. We observe meaningful heterogeneity in the structure and scale of agri-food value chains across commodities and countries. Focusing on traders, we show that the respondent-driven sampling approach generates a larger sample of traders who differ in observable characteristics (i.e., value added, enterprise scale, and financial access) compared to a sub-sample of traders generated in a way that mimics traditional random sampling approaches used to study traders. We conclude by discussing how this respondent-driven sampling approach, applied within transaction-linked networks, can provide a useful data collection method for studying intermediary agri-food value chain actors.

This book provides a comprehensive and interdisciplinary examination of the diverse aspects of climate change in South Asia. The region, home to almost 4% of the world’s population, is under serious threat from climatic disasters. The volume underscores the urgency of addressing cataclysmic events related to climate change and their ramifications on the economy, agriculture and livelihoods of the region. The book discusses the reasons causing climate change as well as highlights normative and ethical considerations involved in the battle against climate change. With case studies from India, Sri Lanka and Bangladesh, it explores issues such as extreme climatic events; energy use, fossil fuels, non-renewable resources and carbon dioxide emission in South Asia; internal migration and climate refugees; the ethical dilemma of sustainable development; technological advancements for extreme weather forecast; and responses to climate change in South Asia. Highlighting the need for striking a balance between developmental imperatives and environmental sustainability, the chapters also show the North-South divide in the research agenda and policies on climate change and the global politics that underlie climate policies. The volume juxtaposes a scientific analysis of factors responsible for climate change with an analysis of the human cost of climate change from the perspective of social sciences. It discusses the challenges faced by developing countries while also offering recommendations and solutions. This book will be of interest to scholars and researchers of climate studies, geography, public policy and governance, sustainable development, development studies, environmental studies, political studies, international relations, political economy, economics and sociology. It will also be useful to practitioners, thinktanks, policymakers and civil society organisations working on environmental management.

With the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI), the traditional methods of farming are undergoing transformation. By harnessing the power of data-driven insights and automation, farmers can now make informed decisions in real time, optimize resource utilization, and mitigate risks associated with crop management and livestock welfare. This book serves as a guide to the integration of IoT and AI in agriculture and discusses the methodologies, applications, and challenges in this rapidly evolving field. It details various aspects of smart farming—from crop monitoring and precision agriculture to livestock management and food supply chain transparency—and provides insight into the potential of IoT and AI to revolutionize agricultural practices and address the global challenges of food security, environmental sustainability, and economic development. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan)

In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).