Advances in Agronomy, Volume 173, the latest release in this leading reference on agronomy, contains a variety of updates and highlights new advances in the field, with each chapter written by an international board of authors.

Assessing Progress toward Sustainability: Frameworks, Tools, and Case Studies provides practical frameworks for measuring progress toward sustainability in various areas of production, consumption, services and urban development as they relate to environmental impact. A variety of policies/strategies or frameworks are available at national and international levels. This book presents an integrated approach to sustainability progress measurement by considering both the frameworks and methodological developments of various tools, as well as their implementation in assessing the sustainability of processes, products and services through a global perspective. Combining methods and their application, the book covers a variety of topics, including lifecycle assessment, risk assessment, nexus thinking, and connection to SDGs. Organized clearly into three main sections --Frameworks, Tools, and Case Studies--this book can serve as a practical resource for researchers and practitioners alike in environmental science, sustainability, environmental management and environmental engineering.

Silicon and Nano-silicon in Environmental Stress Management and Crop Quality Improvement: Progress and Prospects provides a comprehensive overview of the latest understanding of the physiological, biochemical and molecular basis of silicon- and nano-silicon-mediated environmental stress tolerance and crop quality improvements in plants. The book not only covers silicon-induced biotic and abiotic stress tolerance in crops but is also the first to include nano-silicon-mediated approaches to environmental stress tolerance in crops. As nanotechnology has emerged as a prominent tool for enhancing agricultural productivity, and with the production and applications of nanoparticles (NPs) greatly increasing in many industries, this book is a welcomed resource.

Agriculture's Ethical Horizon: Third Edition covers the changing environment in which practitioners of agriculture are challenged to produce food for the world. Fully revised and updated, the book encourages discussions on the moral questions that agriculture faces, including what goals should agricultural science pursue and how should practitioners address important ethical questions which are different and more complex than the dominating questions of production? The book presents the story of agriculture from the blood, sweat and tears era, to the present genetic era, including the paradox of agriculture. This book is ideal for agricultural students, practitioners and anyone who would like to understand the tremendous responsibility of agricultural production. It presents a foundation for the important discussions and decisions that will be necessary to support the future of agriculture.

Advances in Agronomy, Volume 172, the latest release in this leading reference on the topic, contains a variety of updates and highlights new advances in the field, with each chapter written by an international board of authors.

The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm.