Preface With the great explosion in computation and information technology has come vast amounts of data and tools in all fields of endeavour. Soil science is no exception, with the ongoing creation of regional, national, continental and worldwide databases. The challenge of understanding these large stores of data has led to the development of new tools in the field of statistics and spawned new areas such as data mining and machine learning. In addition to this, in soil science, the increasing power of tools such as geographical information systems (GIS), geographic positioning system (GPS), remote and proximal sensors and data sources such as those provided by digital elevation models (DEMs) are suggesting new ways forward. Fortuitously, this comes at a time when there is a global clamour for soil data and information for environmental monitoring and modelling. Consequently, worldwide, organisations are investigating the possibility of applying the new spanners and screwdrivers of information technology and science to the old engine of soil survey. The principal manifestation is soil resource assessment using geographic information systems (GIS), i.e., the production of digital soil property and class maps with the constraint of limited relatively expensive fieldwork and subsequent laboratory analysis. The production of digital soil maps ab initio, as opposed to digitised (existing) soil maps, is moving inexorably from the research phase to production of maps for regions and catchments and whole countries. Rapid and reliable assessment of soil characteristics is an important step in agricultural and natural resource management. In general, soils are opaque to most sensing methods. For example, microwave radiations penetrate only a few centimetres of the topsoil; visible (VIS) and infrared radiations can barely penetrate through the soil surface. Consequently, most soil assessments are performed under laboratory conditions. Laboratory methods used for estimating soil chemical properties are based on wet chemistry with tedious and time-consuming sample preparation and analyses steps. Assessment of soil physical attributes generally takes a longer time than chemical attributes. Soil properties widely vary both in time and space. Consequently, rapid and in situ assessment of soil properties even in near-real time remains a formidable task despite decades of research and development in soil testing. Over the past few decades, remote sensing approaches provide some solution for rapid soil assessment. These approaches are fast, nondestructive and have large spatial coverage. There are four factors that influence the remote sensing (especially optical) signature of soil–mineral composition, organic matter, soil moisture and texture. Remote sensing data have been used for soil classification, soil resources mapping, soil moisture assessment and soil degradation (salinity) mapping among many others. Particularly, hyperspectral remote sensing (HRS) is emerging as a promising tool for its capability to measure the reflectance of earth surface features such as soil, water, vegetation, etc. at hundreds of contiguous and narrow wavelength bands. Availability of such a large pool of spectral information offers an opportunity to estimate multiple soil attributes from the same reflectance spectra with greater specificity than their multispectral counterpart. Keeping in mind the requirement of digital soil maps for efficient management of natural resources, the book on “Soil Resources and its Mapping Through Geostatistics Using R and QGIS” is written. This book will provide an exposure to recent developments in the field of geostatistical modeling, spatial variability of soil resources, and preparation of digital soil maps using R and GIS and potential application of it in agricultural resource management. Specifically following major areas are covered in the book.

Introduction Geostatistics is a branch of statistics that deals with the analysis and modelling of geo-referenced data. Its main aims are to quantify spatial variability and to create maps from point observations. Of course there is much more to geostatistics than just that and indeed some of the other uses of geostatistics are addressed in this chapter too, but the emphasis is on the assessment of spatial variability and geostatistical interpolation. The first step of geostatistical interpolation is to quantify the spatial correlation structure of the variable of interest. This can be done by examining the observations and how these vary in space. Next spatial interpolation makes use of the quantified spatial correlation to derive optimal predictions at unobserved locations and create a map. The interpolation error is quantified as well, which helps to design optimal spatial sampling schemes that balance data collection costs and map accuracy. All this will be explained in this chapter, but in order to do so we first need to explain the statistical theory that underlies geostatistical interpolation followed by representation of the spatial correlation structure, the basics of geostatistical interpolation (‘kriging’), kriging extensions and spatial stochastic simulation.

Introduction A complex spatial soil properties pattern is directly controlled by soil internal factors and anthropogenic impacts.Hartemink et al. (2001) elaborate a strong decline in mineralogy, morphology and genesis of soil research than strong increase in pedometrics applications during 1967 to 2001. Hartemink and McBratney (2008) identified this change as “soil science renaissance”. Basically, the soil science deeply rooted in agriculture, geology, and chemistry but the paradigm has been shifted from classification and inventorization to quantification of soil patterns in spatial as well as temporal patterns and their impact on the ecosystem health and hydrological cycle. The environmental keen approach explains that soil is in the center of the ecosystem and highly interacting with the biotic and abiotic factors of the ecosystem and a complex pattern and process has been co-evolved with the time.

The arid region in India is spread over in 38.7 million hectare land. Out of the total 31.7 m hectare lies in hot arid region and remaining 7 m hectare comes under cold arid region. The hot arid region occupies major part of northwestern India (28.7 m ha) and a small pocket (3.13 mha) in southern India. The north western arid region occurs between 22°30’ and 32°05’ N latitude and 68°05’ to 75°45’ E, covering western part of Rajasthan, north western Gujarat and south western parts of Haryana and Punjab. About 62% area of arid region falls in western Rajasthan and 20% in Gujarat. Haryana and Punjab together constitute 7% area of arid region. The state wise distribution of arid region is shown in Table 3.1. Further our discussion is based on the majority area of arid region, belonging to Rajasthan, Gujarat, Punjab and Haryana.

Soil sampling is an integral and essential component of soil fertility evaluation and nutrient management research. The effectiveness of soil sampling is a prerequisite for soil testing to achieve its goals for efficient nutrient management and plant nutrition for improved yield and quality. The underlying basis for soil sampling is that a soil sample taken represents the “population” which may be a plot, field or a watershed. It further implies that nutrient status of the representative soil sample(s) determined in a laboratory would reflect nutrient status of a plot, field or watershed and is of interest for correcting nutrient disorders in the field or watershed. The most important factor that influences the effectiveness of soil sampling is soil heterogeneity. However, in a relatively homogenous group of fields or plots, a small number of samples may be sufficient to represent the population to a more heterogenous group of fields that would require more number of samples to represent the soil population. Whenever, soil samples to be taken in field following points needs to be kept in mind.

Introduction R is one of the most popular platforms for data analysis and visualization currently available. It is free, open-source software, with versions for Windows, Mac OS X, and Linux operating systems. R is a language and environment for statistical computing and graphics, similar to the S language originally developed at Bell Labs. It’s an open source solution to data analysis that’s supported by a large and active worldwide research community. But there are many popular statistical and graphing packages available e.g. Microsoft Excel, SAS, IBM SPSS, Stata, and Minitab etc., however R has many interesting features so why it is mostly followed by researchers and academicians. Few of the major features of R are given below:

Introduction Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

Introduction The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

Introduction Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

Introduction In geographical location, the observations taken in proximity tend to be more alike than observations made at points that are far apart-that is, the values tend to be correlated. An area of study that takes this sort of spatial consideration into account is called geostatistics. A primary motivation for sampling is to make meaningful estimates of values at nearby positions in space and time. When values are assumed independent, the best estimate for an unmeasured point will be the mean. However, for correlated values nearby values are estimated by interpolation. The simplest interpolation scheme is a nearest neighbor estimate for which the unknown values are based on the closest measured location. Inverse distance is another sort of interpolation technique where weights are estimated based on the inverse distance squared. Kriging is a geostatistical interpolation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas. It makes the best use of existing knowledge by taking account of the way that a property varies in space through the variogram model. In its original formulation a kriged estimate at a place was simply a linear sum or weighted average of the data in its neighbourhood. Weights are based on the distance between the measured points, the prediction locations, and the overall spatial arrangement among the measured points. Since then kriging has been elaborated to tackle increasingly complex problems in mining, petroleum engineering, pollution control and abatement, and public health. The term is now generic, embracing several distinct kinds of kriging, both linear and non-linear.

Introduction Spatial datasets always exhibit two common features (i) the occurrence of a few very large concentrations (hot-spots) and (ii) the presence of data below the detection limit (black spots). Extreme values can strongly affect the characterization of spatial patterns, and subsequently the prediction. Several approaches exist to handle strongly positively skewed histograms (Saito and Goovaerts 2000). One common approach is to first transform the data (e.g. normal, Box Cox or lognormal), perform the analysis in the transformed space, and back-transform the resulting estimates. Such transform, however, does not solve problems created by the presence of numerous black spot data, since either it yields a spike of similar transformed values or, in the case of the normal-score transform, it requires a necessarily subjective ordering of all equally-valued observations. Moreover, except for the normal score transform (Deutsch and Journel 1998), it does not guarantee the normality of the transformed histogram, which is required to compute confidence intervals for the estimates. Last, the back-transform of estimated moments is not straightforward and can introduce bias if not done properly (Saito and Goovaerts 2000); for example, lognormal kriging estimates cannot simply be exponentiated. Another way to attenuate the impact of extreme values is to use more robust statistics and estimators. The non-parametric approach of indicator kriging (IK) falls within that category (Journel 1983, Goovaerts 2001). The basic idea is to discretize the range of variation of the environmental attribute by a set of thresholds (e.g. deciles of sample histogram, detection limit, regulatory threshold) and to transform each observation into a vector of indicators of non-exceedence of each threshold. Kriging is then applied to the set of indicators and estimated values are assembled to form a conditional cumulative distribution function (ccdf). The mean or median of the probability distribution can be used as an estimate of the concentration of the material in question (Barabas et al. 2001, Cattle et al. 2002, Goovaerts et al. 2005).

Introduction Quantum Geographical Information System (QGIS) is an open source GIS environment. The software package is available freely in web https://www.qgis.org/en/site/forusers/download.html) and can be installed in a variety of computer platform e.g. Windows, Linux, Mac OS X, Android etc. Latest information on QGIS is available inhttp://www.qgis.org/en/site/. Basically, the QGIS GUI is divided into five areas: (i) Menu Bar, (ii) Tool Bar (iii) Map Legend, (iv) Map View and (v) Status Bar. QGIS allows users to define a global and project-wise coordinate reference system (CRS) for layers without a pre-defined CRS. It also allows the user to define custom coordinate reference systems and supports on-the-fly (OTF) projection of vector and raster layers. All of these features allow the user to display layers with different CRSs and have them overlay properly. QGIS has support for approximately 2,700 known CRSs. Definitions for each CRS are stored in a SQLite database that is installed with QGIS. The CRSs available in QGIS are based on those defined by the European Petroleum Search Group (EPSG) and the Institute Geographique National de France (IGNF) and are largely abstracted from the spatial reference tables used in Geospatial Data Abstraction Library (GDAL). For Post GIS layers, QGIS use the spatial reference identifier that was specified when the layer was created. For data supported by OGR, QGIS relies on the presence of a recognized means of specifying the CRS. In the following sections, few basic GIS analysis using example spatial data from arid western India is discussed. However, readers are advised to go through different tools and functions available in QGIS after installation of the system in personal computers.

Introduction GIS (Geographical Information System) based mapping takes cogence of three major geometrical dimensions, point, line and polygons and in GIS these three features correlate their relationship through a concept called Topology. Among the various applications where covariate processing requires specific attention, the landuse/ land cover and Digital Elevation Models/surfaces or terrains are the major area of interest. Use and processing of data for these covariates carries importance as accuracy of both these aspects would influence the derivatives extracted out of them further. For example, if landuse is improperly defined, the site for which planning is proposed may not sustain, similarly, point data/elevation data in case of DEMs would decide the exact topography of any landforms for their further utilization. For their use in GIS, while DEMs depend upon point data, the landuse/ land cover will be taken care by polygons. For the purpose of the study, we are going to discuss about these two covariates in the following paragraphs. Generally, Covariate (covariable) (koh-vair-iãt) in statistics is defined as a continuous variable that is not part of the main experimental manipulation but has an effect on the dependent variable. In other words, as per ANCOVA (Analysis of covariance) definition: a covariate is a control variable.

Introduction Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

Introduction Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing. The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Introduction Over the past few decades, soil resources have witnessed many changes driven by demographic pressure and climate change. It is necessary to have up to date soil information to monitor the state of soils health with time. Soil resource inventory provides information about the potentialities and limitation of soils for its proper utilization. Soil survey offers an accurate and scientific inventory of different soils, their properties and extent. An in-depth knowledge of the type of soils and their spatial distribution is a prerequisite for developing rational land use planning. Remote sensing is the science of obtaining information about the earth’s surface without actually being in contact with it. On the other hand, proximal sensing refers to remotely sensed measurements that are taken at the field or laboratory level. Both these processes involve an interaction between incident radiation and the targets of interest. Fundamentally, remote sensing data products are a representation of the functional response of objects to energy sources. These are typically spectra, which are acquired as a discrete series of reflectance measurements taken at different wavelength intervals, or bands. In the shorter wavelengths of the electromagnetic spectrum (visible part), features can be detected by virtue of reflected solar energy, while in the longer wavelengths (microwave, thermal parts), sensing of emitted energy works. Conventional soil sampling, and subsequent laboratory analysis are generally time consuming and costly. In this context, remote sensing plays a crucial role for studying soil resources. The term remote sensing is generally used for airborne and space borne acquisitions, whereas proximal sensing refers to ground-based laboratory and field measurements. Advances in the quantitative disciplines like remote sensing and proximal sensing have laid the foundations for a spatial exploration of soil-system dynamics within a landscape context (Pennock and Veldkamp 2006).

Introduction In India, hot arid ecosystem covering an area of 31.7 million ha lies at north western part of the country and 62% of this area falls in western Rajasthan covering 12 districts i.e. Jodhpur, Jaisalmer, Jhunjhunu, Sikar, Hanumangarh, Sri Ganganagar, Bikaner, Barmer, Pali and Sirohi. The major landforms in arid western Rajasthan are dune and inter-dune plains having sandy to sandy-loam textured soils. Temperature remains high for most part of the year reaching up to 42-45°C during prolonged summer months (April to July), whereas annual rainfall is very low and highly sporadic (100-400 mm) mainly occurs during August and September. Annual potential evapotranspiration ranges from 1500-2000 mm. Loss of fertile top soil from denuded soil surface through wind erosion is frequent during summer months. The region is subjected to frequent occurrence of droughts, once in every three years, which further intensifies the process of desertification (Samra et al. 2006). Shorter growing period, sandy soils, very low available water capacity, inadequate nitrogen and phosphorus availability are the major constraints. Soils are prone to severe wind erosion, terrain deformation and nutrient depletion. The region is mostly mono cropped and double cropping is being practiced extensively in canal and tubes well command area. Extensive fallowing in the past even in very good rainfall years because of uneven undulating topography and severe windstorm are the cause of concern. Ground water is deep and is usually brackish, declining at alarming rate due to excessive withdrawal for irrigation and other purposes. The region, however, sustains on perennial vegetation of Prosopis cineraria, Tecomella undulata, arid shrubs and grasses, which are rich resources of fodder for animal because cropping is affected by vagaries of monsoon. During last two to three decades, a 2 to 3-fold increase in irrigated areas with cultivation of both kharif and rabi crops has been recorded and most of these increments are resulted through conversions from rainfed areas, grazing lands and sand dunes into irrigated areas.

Concept and Definition Desertification is a major problem in the drylands of India, affecting the way of life for its inhabitants. The problem is more severe in the arid lands in the northwestern part of the country. There have been a few numbers of definitions of the word “Desertification”. However for simplicity of its origin and meaning we would refer to two of the frequently used ones; (1) proposed in 1991 by UNEP: “Land degradation in arid, semi-arid and dry sub-humid areas resulting mainly from adverse human impact and (2) prepared for the Earth Summit at Rio, held in June 1992, for inclusion in Agenda 21, as “Land degradation in arid, semi-arid and dry sub-humid areas resulting from climatic variations and human activities. (Anonymous 1992). Comparing two of these initial definitions, it would show that first one indicated desertification as a state of natural resources like soil and vegetation, mainly resulting from human activity while the second one included climate as a major cause. However, it reduced the emphasis of human activities as being the primary responsible factor and tried to link the possible impact of climate change on desertification. But the simplicity of the 1992 definition of desertification helped researchers to bring in sharp focus on the special environmental and socio-economic problems of the drylands. So over the years, the key words that explains the term “desertification” has been changing or are being amended for specific purposes. This would at least mean that the problem of desertification is still debated world-wide, and its impact is not limited to some nations/regions as was presumed and discussed in first of UNCCD platforms. Therefore, as a precaution and combating this process, few catchy words like non-sustainable management, productivity, over-exploitation of natural resources, dryland livelihood, droughts are found to surround the word desertification. Earlier, some, people equated desertification with advancing boundaries of the existing desert and in India, examples of expansion / contraction of geographical boundary of Thar desert was a public debate among various researchers and planners. In the same time, some of the definitions tried to broaden its geographical scope so that greater populations suffering from adverse effects of land degradation would benefit out of various remedial measures.

Land resources units or major land resources units are the suitable segmentations of land, based on available natural resources, their potential use and management. Irrespective of terms used by different organizations or institutes, these units have spatial dimension and represent a composite unit for interstate, regional, and national level planning. A typical meaning of these land units can be related to terms like land system. Christian and Stewart (1953) of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia devised the ‘Land system’ as the composite mapping-unit which has been defined as “an area or group of areas, throughout which there is a recurring pattern of topography, soils and vegetation”. This system is based on the premise that the land system is expressed on aerial photographs by a distinctive pattern and, as such, the land system can be directly mapped from aerial photographs (Christian and Stewart 1953, Mabbutt and McAlpine 1965). It is a fact that during those period, aerial photographs were more frequently used and studies related to land systems used technique of aerial photogrammetry. As per USDA (United States Developmental Agency), the typical segmentations are; (1) Land Resource Region (LRR), which is the highest level in the hierarchy (2) Major Land Resource Area (MLRA), the second highest level and generally representing broad landforms or a geologic region at a small scale and (3) the Land Resource Unit (LRU).

Introduction Soil is a dynamic natural body, characterized by high degree of spatial variability due to combined effect of physical, chemical or biological processes that operate with different intensities at different scales (Goovaerts 1998). Reports have shown that there is large variability in soil properties, crop yield, disease, weed etc., not only in large-sized fields (Godwin and Miller 2003,Vrindts et al. 2005), but also in small-sized fields (Bhattacharya et al. 2008). Therefore, knowledge of hydro-physical properties of soils is crucial for management practices for all kinds of agricultural practices for efficient utilization of input resources. Plant growth and development is highly related to soil hydro-physical properties, which not only depend on soil intrinsic textural and chemical characteristics (Hamza and Anderson 2005) but also on soil management. Soil physical properties which change with the variation in soil structure include bulk density, total porosity, pore geometry (size distribution, shape, continuity and tortuosity), penetration resistance and aggregate stability, consequently affecting soil hydraulic properties such as water retention characteristics, plant available water capacity, infiltration capacity and saturated hydraulic conductivity. These soil properties are dependent on seasonal climatic conditions, management practices, crop development and biological activity (Reynolds et al. 2007). The hydro-physical properties of soils, i.e., water retention and water permeability in both saturated and unsaturated zones not only shape soil water balance but also decide the conditions for plant growth, development and yield. They also determine water availability for the plant root system and the transfer of water with chemical compounds dissolved in it into deeper layers.

Introduction The natural resources of arid regions particularly soil and water are limited and is often in a delicate environmental balance. Desert encroachment due to lack of conservation planning, and the dangers of destroying or depleting beyond recovery these productive resources, are evident at present time and may be disastrous if development is based on short term expediency rather than long term stability. The arid zone of India is spread over 38.7 million ha area, out of which 31.7 m ha in under hot arid region and 7 m ha under cold region. The hot arid region occupies major part of north-western India (28.57 m ha) and occurs in small pockets (3.13 m ha) in south India. The north-western arid region occurs between 22o30' and 32o05' N latitudes and from 68o05' to 75o45' E longitudes, covering western part of Rajasthan (19.6 m ha, 69%), north-western Gujarat (6.22 m ha, 21%) and 2.75 m ha (10%) in south-western part of Haryana and Punjab (Faroda et al. 1999). Rainfall distribution is highly uneven over space and time (CV>60%). The region receives low rainfall (<100 mm to 500 mm), has high evapotranspiration and high temperature regime (Rao and Singh 1998). Groundwater is deep and often brackish. The western-central area is devoid of drainage system and surface water resources are meager (Fig. 20.1). Due to low and erratic rainfall, replenishment of water resources is also very poor. The entire Rajasthan state is being categorized as the driest state and water scarce (having per capita water availability below 1000 m3 year-1) since 1991 in the country. Increasing pollution by industrial units, big and small, unregulated mining and even over-extraction of water from deep wells also add to the water quality problem in number of districts. Rapid urbanization and industrialization make such existing differences even more

Introduction The life support systems of a country and socioeconomic development of its people largely depend on soils. The soils are the most valuable natural resources for the society. It caters the basic needs of mankind by producing food, fibre and timber. It is therefore essential to know the distribution and extent of different soils and their qualities, potentialities and constraints for sustainable utilization of this valuable natural resource (Shyampura and Sehgal 1995). Information on distribution, potential and constraints of major forest soils are essential to design most appropriate soil management systems in order to increase productivity of forests as forests also offer excellent potential for poverty reduction and rural economic growth in India. However, unlike agriculture lands forest soils usually draw less attention and are ignored in spite of the fact that they nourish the lungs of the Earth. Soil quality is the most important factor in forest management decisions. Soils will determine productivity of a particular forest and management strategy. Knowing about forest soils can serve as a basis for forest management decisions, including land acquisition, species selection for planting, site preparation requirements, watershed development, fertilization prescriptions, stand density/ composition, and harvest timing, as well as decisions affecting land ownership and use.

Introduction Microorganisms generally present in all the environments are the most abundant and diverse living organisms present on earth. Microorganisms have great impact on the biosphere due to their inbuilt ability to transform the key elements of life into forms usable by other organisms and plants. Because of heterogeneity in nature of soils from variations in physical, chemical and biological properties the various natural habitat are microbially most diverse environments on the earth maintaining the stability between complex microbial communities and their function (Daniel 2005). One gram of soil contains about 10 billion microorganisms of possibly thousands of different species but microbial diversity in general, and particularly in extreme environments, and its role in ecology is poorly understood (Rossello-Mora and Amann 2001). Deserts present a unique environment with very little precipitation and large variations in day and night temperatures, making it inhospitable environment for living organisms. Still a number of microbial species do survive in those harsh environments. The populations of aerobic bacteria in deserts across the world are reported to vary from < 10 in Atacama desert to 1.6 × 107 cfu g–1 in soils of Nevada (Skujnis 1984), and India is not exception to that with the bacterial population in the range of 1.0 × 103 to 8.35 × 106 cfu g–1 sediment or ml–1 water in the samples collected from cold deserts of north western Himalayas (Yadav et al. 2015).

Introduction High degree of spatial variability in soil properties is observed at different scales. This variability is generally originated from the combined effect of physical, chemical or biological processes operating with different intensities at different scales (Goovaerts 1998). Knowledge of this spatial variationof soil properties is important in several land management applications e.g. variable rate application of nutrient and water in agricultural production system, land suitability classifications, modeling water balance components at landscape scales, precision farming etc. The concept of ‘management zone’ was also evolved in response to this large variability with the main purpose in efficient utilization of agricultural inputs with respect to spatial variation of soils and its properties (Franzluebbers et al. 1996, Atherton et al. 1999, Malhi et al. 2001). Therefore, an appropriate understanding of spatial variation of soil properties is essential. The most important way to gather knowledge in this aspect is to prepare soil maps through spatial interpolation of point-based measurements of soil properties. Here, a case study is discussed where soil water retention at two critical levels e.g. field capacity (FC) and permanent wilting point (PWP) was spatially characterized through geostatistical approaches. Because these two soil moisture contents guide farmers to apply right amount of irrigation water at right time. The study was carried out at the experimental farm of ICAR-Indian Agricultural Research Institute, New Delhi, India (28°37′-28°39′ N, 77°8′ 30″-77°10′30″ E, 217-241 m above mean sea level). A detailed discussion of the study is available in Santra et al. (2008)

Introduction Soil organic carbon (SOC) content plays a key role in maintaining soil fertility in agricultural farm. At present, there has been great interest to reduce the atmospheric CO2 level through a chain of increasing vegetation carbon pools first and then to store them as soil carbon pool. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Average SOC content is considered in most soil carbon pool calculations in spite of its large spatial variation in landscape, and thus leads to inaccurate estimate of SOC stock for an area. Therefore, reliable estimates of SOC pools and their spatial variability are essential to establish the soil carbon sequestration programmes at different landscape scales. Here, a case example of assessing the spatial variation at a typical agricultural farm is discussed. The experimental field was located at Badoda village of Jaisalmer district, Rajasthan covering an area of about 76.12 ha. The farm lies between 265o2′30″-26°53′30″ N and 71°12′15″-71°13′15″ E (Fig. 24.1) and 20 km away from Jaisalmer city in eastward direction. Elevation of the farm ranged from 232 m to 248 m above mean sea level with an overall slope from south to north. Among total area of the farm, 85.38% area was under cultivation in four main blocks: block A, B, C, and D whereas rest area was under grassland.

Introduction Globally, the SOC pool of 1,550 Pg (1 Pg = 1015 g) to 1-m depth is about three times that of biotic and twice that of atmospheric pools (Post et al. 1982, Watson et al. 2000). In arid and semi-arid ecosystems, SOC contents show high degree of spatial variability due to patchiness of vegetation (Wiesmeier et al. 2009). The arid ecosystem might play a key role in mitigation of climate change effects through sequestering carbon in soils (United Nations 2011). Therefore, a suitable land use system having high C sequestration potential for arid ecosystem needs to be identified and promoted. However, the prevailing conditions of arid ecosystems normally does not support dense vegetation. Therefore, low carbon stock is expected in both above-ground vegetation and below-ground soil. Fruit trees are an integral part of arid farming system and played a significant role to improve the nutritional security and employment opportunity of farmers but also have potential role to increase carbon storage both in biomass and soil. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Therefore, reliable estimates of SOC pools are essential to establish the soil carbon sequestration programmes at different landscape scales.

Appendix