Soils of irrigated lands in the Aral Sea Basin are often plagued by high salinity,hampering profitable agriculture on these soils. In this context, the present study has three specific objectives: to identify techniques that enable a rapid estimation of soil salinity, to characterize its spatial distribution and to estimate its spatial distribution based on readily obtainable environmental parametersusing a Neural Network Model Approach. Topsoil salinity was highly variable atshort distances and terrain attributes were the most influential factors. The use of relationships between environmental attributes and soil salinity for upscaling spatial distribution of soil salinity from farm to district level proved to be satisfactory. A possible application is the development of salinity prediction toolsfor farm-level decision making.

The spatial distribution of soil salinity: Detection and prediction. Soils of irrigated lands in the Aral Sea Basin are often plagued by high salinity, hampering profitable agriculture on these soils. In this context, the present study has three specific objectives: to identify techniques that enable a rapid estimation of soil salinity, to characterize its spatial distribution and to estimate its spatial distribution based on readily obtainable environmental parameters using a Neural Network Model Approach. Topsoil salinity was highly variable at short distances and terrain attributes were the most influential factors. The use of relationships between environmental attributes and soil salinity for upscaling spatial distribution of soil salinity from farm to district level proved to be satisfactory. A possible application is the development of salinity prediction tools for farm-level decision making.

Root research under natural field conditions is still a step-child of science. The reason for this is primarily methodological. The known methods are tedious, time consuming, and the accuracy of their results is often not very great. Many research workers have been discouraged by doing such root studies. The need for more information on the development and distribution of plant roots in different soils under various ecological conditions is, however, obvious in many ecological disciplines. Especially the applied botanical sciences such as agriculture, horticulture, and forestry are interested in obtaining more data on plant roots in the soil. This book will give a survey of existing methods in ecological root research. Primarily field methods are presented; techniques for pot experiments are described only so far as they are important for solving ecological problems. Laboratory methods for studying root physiology are not covered in this book. Scientific publications on roots are scattered in many different journals published all over the world. By working through the international root literature I found that about ten thousand papers on root ecology have been published at the present. This is not very much compared with the immense literature on the aboveground parts of the plants, but is, however, too much to cite in this book.

The papers assembled here cover topics such as technological advances in soil salinity mapping and monitoring, management and reclamation of salt-affected soils, use of marginal quality water for crop production, salt-tolerance mechanisms in plants, biosaline agriculture and agroforestry, microbiological interventions for marginal soils, opportunities and challenges in using marginal waters, and soil and water management in irrigated agriculture.

This open access book is an outcome of the collaboration between the Soil and Water Management & Crop Nutrition Section, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency (IAEA), Vienna, Austria, and Dr. Shabbir A Shahid, Senior Salinity Management Expert, Freelancer based in United Arab Emirates.The objective of this book is to develop protocols for salinity and sodicity assessment and develop mitigation and adaptation measures to use saline and sodic soils sustainably. The focus is on important issues related to salinity and sodicity and to describe these in an easy and user friendly way. The information has been compiled from the latest published literature and from the authors’ publications specific to the subject matter. The book consists of six chapters. Chapter 1 introduces the terms salinity and sodicity and describes various salinity classification systems commonly used around the world. Chapter 2 reviews global distribution of salinization and socioeconomic aspects related to salinity and crop production. Chapters 3 covers comprehensively salinity and sodicity adaptation and mitigation options including physical, chemical, hydrological and biological methods. Chapter 4 discusses the efforts that have been made to demonstrate the development of soil salinity zones under different irrigation systems. Chapter 5 discusses the quality of irrigation water, boron toxicity and relative tolerance to boron, the effects of chlorides on crops. Chapter 6 introduces the role of nuclear techniques in saline agriculture.

Recognized and advocated as a powerful tool, the role of remote sensing in identifying, mapping, and monitoring soil salinity and salinization will continue to expand. Remote Sensing of Soil Salinization: Impact on Land Management delineates how to combine science and geospatial technologies for smart environmental management. Choose the Right Tech

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).

The Senegal River valley and delta (SRVD) are affected by long term climate variability. Indicators of these climatic shifts include a rainfall deficit, warmer temperatures, sea level rise, floods, and drought. These shifts have led to environmental degradation, water deficits, and profound effects on human life and activities in the area. Geographic Information Science (GIScience), including satellite-based remote sensing methods offer several advantages over conventional ground-based methods used to map and monitor salt-affected soil (SAS) features. This study was designed to assess the accuracy of information on soil salinization extracted from Landsat satellite imagery. Would available imagery and GIScience data analysis enable an ability to discriminate natural soil salinization from soil sodication and provide an ability to characterize the SAS trend and pattern over 30 years? A set of Landsat MSS (June 1973 and September 1979), Landsat TM (November 1987, April 1994 and November 1999) and ETM+ (May 2001 and March 2003) images have been used to map and monitor salt impacted soil distribution. Supervised classification, unsupervised classification and post-classification change detection methods were used. Supervised classifications of May 2001 and March 2003 images were made in conjunction field data characterizing soil surface chemical characteristics that included exchange sodium percentage (ESP), cation exchange capacity (CEC) and the electrical conductivity (EC). With this supervised information extraction method, the distribution of three different types of SAS (saline, saline-sodic, and sodic) was mapped with an accuracy of 91.07% for 2001 image and 73.21% for 2003 image. Change detection results confirmed a decreasing trend in non-saline and saline soil and an increase in saline-sodic and sodic soil. All seven Landsat images were subjected to the unsupervised classification method which resulted in maps that separate SAS according to their degree of salinity. The spatial distribution of sodic and saline-sodic soils has a strong relationship with the area of irrigated rice crop management. This study documented that human-induced salinization is progressively replacing natural salinization in the SRVD. These pedologic parameters obtained using GIScience remote sensing techniques can be used as a scientific tool for sustainable management and to assist with the implementation of environmental policy.