Soil Organic Carbon Mapping Cookbook

The Soil Organic Carbon Mapping cookbook provides a step-by-step guidance for developing 1 km grids for soil carbon stocks. It includes the preparation of local soil data, the compilation and pre-processing of ancillary spatial data sets, upscaling methodologies, and uncertainty assessments. Guidance is mainly specific to soil carbon data, but also contains many generic sections on soil grid development, as it is relevant for other soil properties. This second edition of the cookbook provides generic methodologies and technical steps to produce SOC maps and has been updated with knowledge and practical experiences gained during the implementation process of GSOCmap V1.0 throughout 2017. Guidance is mainly specific to SOC data, but as this cookbook contains generic sections on soil grid development it can be applicable to map various soil properties.

This document presents the technical details of the first-ever country-driven Global Soil Organic Carbon Map (GSOCmap). This map allows the estimation of Soil Organic Carbon (SOC) stocks from 0 to 30 cm. It represents a key contribution to the Sustainable Development Goal (SDG) indicator 15.3.1, which defines the area of degraded land. The novelty of this map is the fact that it is the first Global SOC stocks assessment which is produced through a participatory approach. Supported by the GSP-Secretariat, countries developed their capacities and stepped up efforts to compile or collect all available soil information at the national level. This technical report is a companion report to the GSOCmap V1.6.0. It presents methodologies and process of compiling the Global Soil organic Carbon Map (GSOCmap).

GSOCmap is the first global soil organic carbon map ever produced through a consultative and participatory process involving member countries, which makes this map totally new and unique. In fact, the map was prepared by member countries, under the guidance of the Intergovernmental Technical Panel on Soils and the Global Soil Partnership Secretariat. Countries agreed on the methodology to produce the map and were trained on modern tools and methodologies to develop national maps. The Global Soil Partnership then gathered all national maps to produce the final product, ensuring a thorough harmonization process. This technical report is a companion report to the GSOCmap V1.5. It presents methodologies and process of compiling the Global Soil Organic Carbon Map.

This document presents the technical details of the first ever country-driven Global Soil Organic Carbon Sequestration Potential Map (GSOCseq). This map allows for the estimation of top (0–30 cm) soil organic carbon sequestration potential in agricultural areas under a business as usual and three sustainable soil management scenarios. The Global Soil Organic Carbon Sequestration Potential Map (GSOCseq) stands out as a game-changing program aimed at bridging this divide by raising technical expertise on SOC sequestration potential modeling and mapping while relying on a uniquely participatory and iterative process. The GSOCseq v1.1 was developed based on the submissions of national experts appointed by the Food and Agriculture Organization of the United Nations (FAO) Member Nations. Each of the appointed National Experts generated national maps following a bottom-up approach that was facilitated and coordinated by the Secretariat of FAO’s Global Soil Partnership (GSP).

Responding to a request for support in addressing the Sustainable Development Goal Indicators, especially Indicator 15.3 which includes the restoration of degraded soils, the Global Soil Partnership (GSP) Plenary Assembly in 2020 instructed the Intergovernmental Technical Panel on Soils (ITPS) and the GSP Secretariat to develop the Global Soil Organic Carbon Sequestration Potential map (GSOCseq map), following the same country-driven approach developed for the Global Soil Organic Carbon map (GSOCmap). This ‘bottom-up’ approach is expected to generate a GSOCseq map from national SOCseq maps, developed and validated by local experts, based on the implementation of SOC models using standardized procedures and by leveraging the best available local data.

This book addresses the threats that hamper the achievement of sustainable soil management: soil erosion by water and wind, soil organic carbon loss, nutrient imbalance, salinization, contamination, acidification, loss of soil biodiversity, surface sealing, compaction and water logging. The specific focus is on preventive and ameliorative strategies for sustainable soil management.

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.

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