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This volume deals with land degradation, which is occurring in almost all terrestrial biomes and agro-ecologies, in both low and high income countries and is stretching to about 30% of the total global land area. About three billion people reside in these degraded lands. However, the impact of land degradation is especially severe on livelihoods of the poor who heavily depend on natural resources. The annual global cost of land degradation due to land use and cover change (LUCC) and lower cropland and rangeland productivity is estimated to be about 300 billion USD. Sub-Saharan Africa (SSA) accounts for the largest share (22%) of the total global cost of land degradation. Only about 38% of the cost of land degradation due to LUCC - which accounts for 78% of the US$300 billion loss – is borne by land users and the remaining share (62%) is borne by consumers of ecosystem services off the farm. The results in this volume indicate that reversing land degradation trends makes both economic sense, and has multiple social and environmental benefits. On average, one US dollar investment into restoration of degraded land returns five US dollars. The findings of the country case studies call for increased investments into the rehabilitation and restoration of degraded lands, including through such institutional and policy measures as strengthening community participation for sustainable land management, enhancing government effectiveness and rule of law, improving access to markets and rural services, and securing land tenure. The assessment in this volume has been conducted at a time when there is an elevated interest in private land investments and when global efforts to achieve sustainable development objectives have intensified. In this regard, the results of this volume can contribute significantly to the ongoing policy debate and efforts to design strategies for achieving sustainable development goals and related efforts to address land degradation and halt biodiversity loss.
This highly illustrated reference work covers the three principal types of surface technologies that best protect engineering devices and products: diffusion technologies, deposition technologies, and other less commonly acknowledged surface engineering (SE) techniques. Various applications are noted throughout the text and additionally whole chapters are devoted to specific SE applications across the automotive, gas turbine engine (GTE), metal machining, and biomedical implant sectors. Along with the benefits of SE, this volume also critically examines SE's limitations. Materials degradation pathways - those which can and those which cannot be mitigated by SE - are rigorously explained. Written from a scientific, materials engineering perspective, this concise text is supported by high-quality images and photo-micrographs which show how surfaces can be engineered to overcome the limits of conventionally produced materials, even in complex or hostile operating environments. This book is a useful resource for undergraduate and postgraduate students as well as professional engineers.
In order to assess the environmental exposure from chemicals in various media, you must know the rate at which a chemical will degrade. Handbook of Environmental Degradation Rates saves you the time and money collecting and evaluating this important information. The Handbook provides rate constant and half-life ranges for various processes and combines them into ranges for different media (air, groundwater, surface water, soils), which can be directly entered into various models. Some of the processes the Handbook includes are aerobic and anaerobic biodegradation, direct photolysis, hydrolysis, and reaction with various oxidants or free radicals (e.g., hydroxyl radical and ozone in the atmosphere). Experimental data are used and cited when available, and validated estimation methods are used when no experimental data are available. Researched and organized by leading experts, Handbook of Environmental Degradation Rates is easy-to-use and is well indexed by chemical name and CAS Number.
Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.