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

This book aims to provide a thorough understanding of the concepts in soil and resource inventory - Concepts - Standard soil survey - Scope and objectives – Soil systematic - Soil mapping units - Methods and types of soil survey - Soil maps. Soil Classification - Modern Soil Taxonomy - USDA System - Diagnostic horizons- Soil orders . Soil survey reports-Soil Survey Interpretations - Land Capability Classification - Soil and Land Irrigability Classification Storie’s Index Rating - Fertility Capability Classification- Land suitability for field crops, horticultural crops and forest trees - Land Use Planning concepts and objectives. The book has been compiled in order to provide sound knowledge and to improve the technical competence of the Bachelor Degree of Agriculture students based on Undergraduate curriculum. The topics and contents have been presented to make it easy understanding for the students.