Managing Natural Resources for Sustainable Livelihoods

Managing Natural Resources for Sustainable Livelihoods
Author: Barry Pound
Publsiher: IDRC
Total Pages: 273
Release: 2003
Genre: Electronic books
ISBN: 9781844070268

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First Published in 2003. Routledge is an imprint of Taylor & Francis, an informa company.

Participatory Research and Development for Sustainable Agriculture and Natural Resource Management

Participatory Research and Development for Sustainable Agriculture and Natural Resource Management
Author: Julian F. Gonsalves
Publsiher: IDRC
Total Pages: 273
Release: 2005
Genre: Technology & Engineering
ISBN: 9781552501818

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Intended for aspiring and new practitioners of Participatory Research and Development (PR&D) as well as field-based researchers in developing countries. Highlights that agricultural research and development has become a joint approach to deal with diverse biophysical environments, multiple livelihood goals, rapid changes in local and global economies, and an expanded range for stakeholders over agriculture and natural resources.

Sustainable Resource Management

Sustainable Resource Management
Author: Chaudhery Mustansar Hussain,Juan F. Velasco-Munoz
Publsiher: Elsevier
Total Pages: 470
Release: 2021-06-20
Genre: Science
ISBN: 9780128243435

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Sustainable Resource Management: Modern Approaches and Contexts presents the application of the current concept of sustainability to the management of natural resources, such as water, land, minerals and metals using theoretical field knowledge and illustrative real-world examples. Initially, the book defines sustainability, detailing its evolution and how it has been adapted to each of the contexts in which it is used. Furthermore, sustainability is made up of three main areas of science—environmental, social and economic—which are rarely considered together. This book is a complete reference guide to sustainability of natural resources for academics, researchers, practitioners and postgraduate-level students, and more. As sustainability is an interdisciplinary field, linked to most sciences, it is also of use to all fields of science that need to maintain sustainable practices and specific details on the methodologies and techniques needed for sustainable resource management. Provides an integrated approach for modern tools, methodologies and indicators for sustainable resource management Evaluates emerging trends and advanced approaches in sustainable resource management, detailing the most up-to-date research and management considerations Describes advanced sustainable resource management technologies and presents case studies where applicable

Sustainability in Natural Resources Management and Land Planning

Sustainability in Natural Resources Management and Land Planning
Author: Walter Leal Filho,Ulisses Miranda Azeiteiro,Andréia Faraoni Freitas Setti
Publsiher: Springer Nature
Total Pages: 550
Release: 2021-08-06
Genre: Science
ISBN: 9783030766245

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This book includes contributions from scientists and representatives from government and non-governmental organisations working in the field of land management and use and on management of fires. The book is truly interdisciplinary and has both a research and application-oriented dimension. The list of topics includes sustainability and water management; sustainability and biodiversity conservation; the future sustainability of nature-based industries such as agriculture, mining, tourism, fisheries and forestry; sustainability, people and livelihoods; sustainability and landscapes planning; sustainability and land use planning; handling and managing forest fires. The papers are innovative and cross-cutting, and many have practice-based experiences. Also, this book, prepared by the Inter-University Sustainable Development Research Programme (IUSDRP) and the World Sustainable Development Research and Transfer Centre (WSD-RTC), reiterates the need to promote a sustainable use of land resources today.

Sustainable Natural Resource Management

Sustainable Natural Resource Management
Author: Daniel R. Lynch
Publsiher: Cambridge University Press
Total Pages: 249
Release: 2009-03-02
Genre: Business & Economics
ISBN: 9780521899727

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Natural resources support all human productivity. The sustainable management of natural resources is among the preeminent problems of the current century. Sustainability and the implied professional responsibility start here. This book uses applied mathematics familiar to undergraduate engineers and scientists to examine natural resource management and its role in framing sustainability. Renewable and nonrenewable resources are covered, along with living and sterile resources. Examples and applications are drawn from petroleum, fisheries, and water resources. Each chapter contains problems illustrating the material. Simple programs in commonly available packages (Excel, MATLAB) support the text. The material is a natural prelude to more advanced study in ecology, conservation, and population dynamics, as well as engineering and science. The mathematical description is kept within what an undergraduate student in the sciences or engineering would normally be expected to master for natural systems. The purpose is to allow students to confront natural resource problems early in their preparation.

Sustainable Resource Management

Sustainable Resource Management
Author: Peter N. Nemetz
Publsiher: Edward Elgar Publishing
Total Pages: 464
Release: 2007
Genre: Business & Economics
ISBN: STANFORD:36105123376274

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This volume examines some of the remarkable achievements on the long road to sustainable development. More importantly it lays out in detail many of the extraordinary challenges remaining.

Sustainable Resource Management

Sustainable Resource Management
Author: Stefan Bringezu,Raimund Bleischwitz
Publsiher: Routledge
Total Pages: 288
Release: 2017-09-08
Genre: Business & Economics
ISBN: 9781351279260

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Looking at material flows, industrial and societal metabolism and their implications for the economy, this book provides radical perspectives on how the global economy should use natural resources in intelligent ways that maximise well-being without destroying life-supporting ecosystems.

Machine Learning for Ecology and Sustainable Natural Resource Management

Machine Learning for Ecology and Sustainable Natural Resource Management
Author: Grant Humphries,Dawn R. Magness,Falk Huettmann
Publsiher: Springer
Total Pages: 441
Release: 2018-11-05
Genre: Science
ISBN: 9783319969787

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Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.