Predictive Soil Mapping (PSM) is based on applying statistical and/or machine learning techniques to fit models for the purpose of producing spatial and/or spatiotemporal predictions of soil variables i.e. maps of soil properties and classes at different resolutions. It is a multidisciplinary field combining statistics, data science, soil science, physical geography, remote sensing, geoinformation science and a number of other sciences. Predictive Soil Mapping with R is about understanding the main concepts behind soil mapping, mastering R packages that can be used to produce high quality soil maps, and about optimizing all processes involved so that also the production costs can be reduced. The online version of the book is available at: https: //envirometrix.github.io/PredictiveSoilMapping/ Pull requests and general comments are welcome. These materials are based on technical tutorials initially developed by the ISRIC's Global Soil Information Facilities (GSIF) development team over the period 2014-2017

Timely and reliable information on natural resources, regarding their potential and limitations, is a prerequisite for sustainable development. Geospatial technologies offer immense potential in providing such information in a timely and cost-effective manner. Using orbital sensors data in conjunction with airborne and proximal sensors data to generate information on soils and agricultural resources, forests, mineral resources, fossil fuel, wetlands, water resources, and marine resources, this book focuses on the advancements in technologies applicable to managing these resources. It addresses global issues like climate change and land degradation neutrality and introduces spatial data infrastructure (SDI) as a mechanism for sharing geospatial data. This book also provides an in-depth discussion on drones, crowdsourcing, cloud computing, Internet of Things, machine learning, and their applications. FEATURES Contains a comprehensive resource on the latest developments in geospatial technologies and their use in monitoring natural resources, productivity mapping, and modeling Explains the geo-computation methods and online algorithm developments Includes clear guidance on how best to use geospatial data for various applications Discusses case studies from a variety of fields and current trends in the management of natural resources Provides future scenarios concerning platforms, sensors, data analysis, and interpretation techniques This book is written for remote sensing and GIS professionals in environmental institutions and government who are involved in natural resource management projects. Senior undergraduate and graduate-level students in Earth sciences, geography, or environmental management can also use this text for supplementary reading.

Digital Soil Mapping is the creation and the population of a geographically referenced soil database. It is generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships. Digital soil mapping is advancing on different fronts at different rates all across the world. This book presents the state-of-the art and explores strategies for bridging research, production, and environmental application of digital soil mapping.It includes examples from North America, South America, Europe, Asia, and Australia. The chapters address the following topics: - evaluating and using legacy soil data - exploring new environmental covariates and sampling schemes - using integrated sensors to infer soil properties or status - innovative inference systems predicting soil classes, properties, and estimating their uncertainties - using digital soil mapping and techniques for soil assessment and environmental application - protocol and capacity building for making digital soil mapping operational around the globe.

This textbook emphasizes a diversity of values from different cultures, religions, and geographical locations. The book is designed to assist students, computing professionals, and faculty members to act in a more professional and ethical manner. Compelling case studies, ethical reasoning, and cultural perspectives will be included throughout the book, and the authors will apply lessons learned over many years of intense involvement in computing ethics. The text is appropriate either as a main text in a stand-alone ethics course or as a supplementary text for other related courses.

This book describes and provides many detailed examples of implementing Digital Soil Mapping (DSM) using R. The work adheres to Digital Soil Mapping theory, and presents a strong focus on how to apply it. DSM exercises are also included and cover procedures for handling and manipulating soil and spatial data in R. The book also introduces the basic concepts and practices for building spatial soil prediction functions, and then ultimately producing digital soil maps.

An essential resource work for understanding how to design and develop smart applications for present and future problems of the field of agriculture.— Dr. Deepak Gupta, Maharaja Agrasen Institute of Technology, Delhi, India As a result of the advances in Artificial Intelligence (AI), many aspects of daily life have been transformed by smart digital technology. Advanced intelligent algorithms can provide powerful solutions to real-world problems. Smart applications have become commonplace. All areas of life are being changed by smart tools developed to deal with complex issues challenging both humanity and the earth. Artificial Intelligence and Smart Agriculture Applications presents the latest smart agriculture applications developed across the globe. It covers a broad array of solutions using data science and AI to attack problems facing agriculture worldwide. Features: Application of drones and sensors in advanced farming A cloud-computing model for implementing smart agriculture Conversational AI for farmer's advisory communications Intelligent fuzzy logic to predict global warming’s effect on agriculture Machine learning algorithms for mapping soil macronutrient elements variability A smart IoT framework for soil fertility enhancement AI applications in pest management A model using Python for predicting rainfall The book examines not only present solutions but also potential future outcomes. It looks at the role of AI-based algorithms and the almost infinite combinations of variables for agricultural applications. Researchers, public and private sector representatives, agriculture scientists, and students can use this book to develop sustainable and solutions for smart agriculture. This book’s findings are especially important as the planet is facing unprecedented environmental challenges from over-farming and climate change due to global warming.

The book compiles the main ideas and methodologies that have been proposed and tested within these last fifteen years in the field of Digital Soil Mapping (DSM). Begining with current experiences of soil information system developments in various regions of the world, this volume presents states of the art of different topics covered by DSM: Conception and handling of soil databases, sampling methods, new soil spatial covariates, Quantitative spatial modelling, Quality assessment and representation of DSM outputs. This book provides a solid support to students, researchers and engineers interested in modernising soil survey approaches with numerical techniques. It is also of great interest for potential soil data users.* A new concept to meet the worldwide demand for spatial soil data * The first compilation of ideas and methodologies of Digital Soil Mapping * Offers a variety of specialities: soil surveying, geostatistics, data mining, fuzzy logic, remote sensing techniques, Geographical Information Science,...* Written by 82 researchers from 13 different countries

Signi?cant technological advances have been few and far between in the past approximately one hundred years of soil survey activities. Perhaps one of the most innovative techniques in the history of soil survey was the introduction of aerial photographs as base maps for ?eld mapping, which replaced the conventional base map laboriously prepared by planetable and alidade. Such a relatively simple idea by today’s standards revolutionized soil surveys by vastly increasing the accuracy and ef?ciently. Yet, even this innovative approach did not gain universal acceptance immediately and was hampered by a lack of aerial coverage of the world, funds to cover the costs, and in some cases a reluctance by some soil mappers and cartog- phers to change. Digital Soil Mapping (DSM), which is already being used and tested by groups of dedicated and innovative pedologists, is perhaps the next great advancement in delivering soil survey information. However, like many new technologies, it too has yet to gain universal acceptance and is hampered by ignorance on the part of some pedologists and other scientists. DSM is a spatial soil information system created by numerical models that - count for the spatial and temporal variations of soil properties based on soil - formation and related environmental variables (Lagacheric and McBratney, 2007).

As the world’s population continues to expand, maintaining and indeed increasing agricultural productivity is more important than ever, though it is also more difficult than ever in the face of changing weather patterns that in some cases are leading to aridity and desertification. The absence of scientific soil inventories, especially in arid areas, leads to mistaken decisions about soil use that, in the end, reduce a region’s capacity to feed its population, or to guarantee a clean water supply. Greater efficiency in soil use is possible when these resources are properly classified using international standards. Focusing on arid regions, this volume details soil classification from many countries. It is only once this information is properly assimilated by policymakers it becomes a foundation for informed decisions in land use planning for rational and sustainable uses.

The contributed volume assimilates the knowledge, experience, and exciting aspects of soil carbon research in the Indian Himalayan region. It includes different aspects and factors associated with soil carbon sequestration in the region, one of the biodiversity hot spots and highly vulnerable to climatic change impacts. Information on different aspects of soil organic carbon dynamics concerning adaptive land management practices and anthropogenic impacts is covered. Further topics include applying advanced tools and techniques to soil carbon vis-a-vis soil erosion research. This book is of interest to researchers and policymakers involved in soil carbon research and offer ideas to enhance the soil carbon in the region concerned. In addition, the book will provide up-to-date information for researchers interested in soil carbon research for the maintenance of soil quality and fertility in the climate-vulnerable Indian Himalayan region.