eChapters

Purpose

The determination of water holding capacity in soils is important as it gives an idea of the capacity of soil to hold water for the use by crops. The light soils which do not hold such water require more frequent irrigations than heavy clay soils; well decomposed organic matter increases the water holding capacity. Exchangeable sodium and type of clay mineral also have a marked effect on water holding capacity. Water holding capacity of soils is useful for selection of soils for irrigability classification. It also helps for comparing other properties of soils.

Particle density determination is based on the measurements of weight of solids and their volume. The weight of solids is obtained by weighing a sample of oven dried soil. The volume of solids is determined is determined by immersion. The method outlined here is “Pycnometer method”.

Principle

A given amount of dry soil when immerses in a definitive volume of water, expels air and results in the displacement of equal volume of water. The volume of soil particles is determined by measuring the volume of water displaced in the pycnometer bottle.

Purpose

The bulk density varies indirectly with total pore space present in the soil and gives a good estimate of porosity of soil. Bulk density is of great importance than particle density in understanding the physical condition of soil.

Soil bulk density is defined as the ratio of the mass of the oven dry soil to its bulk volume.

Method of determination

Different methods are available for bulk density determination which differ how the soil sample is obtained and its volume is determined such as core, clod and excavation methods. There are other methods for the determination of bulk density where different principles are employed, for example, radiation method. Commonly used methods are weighing bottle, core and clod method.

The method is based on the Stokes law which states that the rate of fall of particles in suspension is directly proportional to their size. Accordingly, larger particle (sand) will settle first, followed by silt and clay. The resistance offered by the liquid to the fall of the particles varies with the radius of the sphere.

Principle

This method is based on the principle of dispersion and sedimentation techniques, employed to a given weight of soil sample. Mechanical stirring disperses macro aggregates while chemical dispersing required for micro aggregates. The mixture of dispersed soil particles in water is called a soil suspension. Sedimentation refers to the settling rates of the dispersed particles in water, which is a function of particle size and is governed by Stoke’s law.

Determination of pH is actually measurement of hydrogen ions activity in soil-water system. It is defined as negative logarithm of the hydrogen ion activity. Mathematically, it is expressed

as:pH = - log a H⁺

The pH value of a soil is an indication of soil reaction i.e. acidic, neutral or alkaline. The nutrient availability is governed by soil reaction. It is maximum at neutral pH and decreases with increase in acidity or alkalinity. Thus, pH value gives an idea about the availability of nutrients to plants.

Principle

The pH is a measure of hydrogen or hydroxyl ion activity of the soil-water system. It indicates whether the soil is acidic, neutral or alkaline in reaction. Since crop growth suffers much under both very low (strongly acidic) as well as very high pH (alkaline) conditions.

Now a days, most of the pH meters have single combined electrode. Before measuring the pH of the soil, the instrument has to be calibrated with standard buffer solution of known pH. Since, the pH is also affected by the temperature, hence, the pH meter should be adjusted to the temperature of the solution by temperature correction knob.

Soil testing involves an estimation of its available nutrient status i.e. the portion or amount of nutrient directly available in soil for subsequent uptake by crop plant. This exercise is commonly referred to as soil testing and is used to arrive at optimum fertilizer application ratio. The need for estimation of available nutrient arises because only a small fraction of what the soil contains is the total nutrient content of the soil. Soil test are calibrated by correlating them with crop response and the result from the basis for making fertilizer recommendations.

Estimation of nutrient contents and forms in materials that are involved in nutrient supply and dynamics is a conical step towards planning scientific nutrient management. In this content, both soil and plant testing information comes out of the interpretation of analysis assumes a greater value when their concentrations and amounts can relate to soil fertility, nutrient availability, plant growth, yield and quality of the crop produce.

Soil temperature plays an important role in plant growth. Solar radiation received on the earth surface raises the temperature of the soil surface from morning to solar noon followed by a decline until the next morning. Soil temperature gradients established on the surface lead to flow of heat downwards causing a rise in temperature of lower layers. These variations in temperature affect germination, emergence and further growth of plants. Several types of thermometers such as mercury or liquid in glass, bimetallic, bourdon gauge, thermocouples and electrical resistance have been used in soil research but the use of thermocouples and thermistors has been found most useful. A brief description of different types of thermometers used in measurement of soil temperature is given below:

Liquids in Glass Thermometer

A liquid-in-glass thermometer (Figure 20.1) consists of a capillary glass tube with a bulb at one end filled with a thermometric liquid (mercury), vacuumed and sealed. By reading the position of the liquid level on a scale the temperature value can be obtained.

The ratio of soil air-to-soil water permeability indicates the magnitude of breakdown of structure as a result of wetting. A value of one indicates no change in structure and a value greater than one reflects a deterioration of soil structure.

Principle

The permeability of soil to air can be obtained essentially in the same way as the permeability of soil to water. The measurement of permeability of soil to air involves the preparation and placement of the soil in a suitable container with provision for monitoring pressure differential across the sample and the rate of flow of air through it. For steady state case, this involves measurement of inlet and outlet pressures and the volume flux of air. The suitable form of the Darcy’s equation for calculating permeability of soil to air is:

Soil aeration signifies the ability of a soil to supply oxygen to actively growing roots and soil organisms. Because of constant biological activity causing oxygen consumption and carbon dioxide evolution, the oxygen present in a soil pore or oxygen renewal by mass flow becomes inadequate to meet the plant demand. A continuous diffusion of oxygen from atmosphere to soil and to plant roots through the wetted pores plays an important role. The oxygen reaching plant roots has to diffuse not only through the gaseous phase in the soil pore and wetted pore neck but also through a water film that is present on the hydrated root surface.Lemon and Erickson (1952) suggested a platinum microelectrode method that simulates the field situation. In this method measurement of the rate of oxygen diffusing through the liquid phase to a reducing surface similar to the plant root.

Infiltration rate refers to the volume of water entering into the soil per unit area in the unit time, when the soil is subjected to a shallow depth of ponding at the surface. Under continued ponding, the high infiltration rate in the beginning decreases to a constant value, known as final infiltration rate. The reduction in infiltration rate is due to combined effects of reduced hydraulic gradients, dispersion of aggregates, blocking of pores, cracks and channels, and swelling of colloids. Numerous techniques for measurement of infiltration rate have been developed to meet specific needs but use of double-ring infiltrometer has given consistent result in a variety of conditions.

Principle

In double-ring infiltrometer method, a small representative area in the field is bounded by two concentric metal rings (Figure 17.1) which permit impounding of water over the soil within the rings. The rate fall of surface of water ponded on the soil in the inner ring is taken as the rate of water intake or infiltration rate. In the cropped field, infiltration rate can be measured only within the rows and for which the diameter of the cylinders may be changed to meet the specific needs. The measurements under wetland conditions should be made from a distance of about 40-50 cm to avoid pressure effects.

Hydraulic conductivity is an important soil property that determines the ability of a soil to transmit water. A method of measuring hydraulic conductivity of saturated soil using undisturbed soil core is described in this exercise.

Principle

Flow of water across a soil layer or a sample of known dimension can be best described by Darcy’s law expressed as:

Water holding capacity of soil is the amount of water that a given soil can hold against the force of gravity. Soil texture and organic matter are the key components that determine the water holding capacity of a soil. Soils with smaller particle sizes (silt and clay) have larger surface area and can hold more water as compared to sand which has large particle sizes and smaller surface area. It is an important hydro-physical characteristic of a soil because it provides simple means of determining useful moisture levels to maintain soil for good plant growth. Detail procedure for determination of water holding capacity of soil using Keen box method (Piper, 1966) is described here.

Over most of the wetness range in which plant roots normally function, all the water in soil is controlled by matric forces. A relationship between water content and matric or soil water suction is referred to as water characteristics of the soil. Increase in matric suction is associated with decrease in water content in the soil and the process is known as desorption. On the other hand with decrease in suction soil moisture content increases and this process is known as sorption. The curves showing relationship during progressive intake and withdrawal of water are known as sorption and desorption curves respectively.

Tensiometer is suitable for finding out soil water characteristic at tension values below 2.0 kPa, whereas gas phase pressure is required for higher tensions. A pressure chamber containing a porous membrane or a ceramic plate with all its accessories to provide regulated compressed air is most convenient. A detailed methodology to determine water characteristic curve by pressure plate apparatus is described below.

As water enters the soil, air is displaced and the soil pores in the upper part of the profile become saturated. A continuous supply of water leads to an increasing depth of saturation. If the supply of water at the soil surface is cut off then the rapid downward movement of water may essentially ceased within 48 hours. The soil is then said to be at its field capacity. At this stage macropores are drained off but micropores are still filled with water for use by plants. Since it is difficult to draw a boundary line between macro-and micropores and there is no sudden change in the profile drainage rate for many soils, it is not easy to specify a depth and time after which soil water movement is negligible. Therefore, measurements are necessary in each soil to specify the time and depth of sampling after wetting. Special care would be necessary in soils with impeded drainage and sand lenses, which produce perched water tables.

Tensiometer is a device to measure soil moisture tension or equivalent negative pressure of water in the soil. A tensiometer consists of a tube sealed at one end by a porous ceramic cup which is in contact with the soil and a vacuum gauge or a manometer connected at the other end above the soil surface. When the tensiometer is filled with water, the manometer or gauge will read zero tension. When such a tensiometer is placed in an unsaturated soil, the soil in contact with the porous ceramic cup will immediately start sucking water through the porous cup wall. The water in the soil pores immediately establishes hydraulic equilibrium with the water in the cup through saturated pores of the cup wall. Water and solutes can f low through the pores of the cup wall but not the air within the working range of the tensiometer. Water could be sucked from the tensiometer by the soil until vacuum created inside the tensiometer is just sufficient to overcome the suction of the soil. At equilibrium, the tensiometer reads directly the amount of soil suction. The outf low of water from the cup is very little. The effective range of the tensiometer is 0 to 0.85 bars depending on quality of the porous cup.

Soil pores differ greatly in size, shape, tortuosity, and continuity. They are characterized generally in terms of total porosity, air-filled porosity, and pore-size distribution. The characterization of pore space is important in understanding the storage and movement of water and gases, the development of plant root systems, the flow and retention of heat, and soil strength. The observation that bulk density is considerably lower than the average particle density indicates that only part of the bulk volume is occupied by soil solids and the remainder is occupied by a lighter material, such as air and water.

Total Porosity

Total porosity (n) is the percentage of the bulk volume not occupied by solids and can be computed using following relationship:

Soil consistency refers to the resistance offered by soil against forces that tend to deform it. The soil consistency varies with texture, kind of clay and moisture content. The efficiency of agricultural implements, animals and labour is largely affected by soil consistency as it directly inf luences the workability of a soil. Operations at suboptimal or supraoptimal water contents lead to clod formation, compaction, poor drainage and aeration. The common terms suggested by Atterberg (1912) to describe consistency are liquid limit, plastic limit, plasticity index, and sticky point. The physical basis of consistency limits is related to the interaction of water with soil. The water content at which plasticity is exhibited is the plastic limit or lower plastic limit of soil.

10.1 Liquid limit

Principle

The liquid limit refers to the water content at which resistance to f low becomes negligible and soil represents a semi f luid. A trapezoidal groove of specified shape cut soil held in a special cup will close in 25 taps on a hard rubber plate. The soil in the cup is 1.0 cm deep and the groove is 2.0 mm wide at the bottom with slopes outward at a 60^° angle horizontally. The soil in the walls of the groove f lows under the impact of the cup on the hard base and close the groove.

The variable amount of moisture contained in a unit mass or volume of soil and is the key factor affecting the growth of plants. Numerous other soil properties depend very strongly upon moisture content. Gravimetric (direct) and sensor based (indirect) methods are generally employed for soil moisture determination.

9.1 Gravimetric Method (Direct)

Principle

The gravimetric method is based on the principle that the water in the porous material is lost by evaporation on heating and the porous material undergoes an equivalent loss in its weight. However, the water loss may be limited by the internal operation within the complex porous material such as soil. This method is simple, routine, reliable, inexpensive, and easy to use. The major limitations of this method are that it is destructive, laborious and time consuming.

Breaking of soil aggregates and dispersion followed by a rapid drying leads to rearrangement and coherence of soil solids in the form of surface crust. The cohesion exhibited by soil upon drying is directly related to surface crusting and clod formation. The formation of soil crusts and hard clods is a major structural feature of soils. Seedling emergence of cereal crops is generally controlled by point resistance around the shoot tip (Richards, 1953). Penetrometer, which measures the resistance to vertical penetration in soil, is an effective instrument to evaluate the crust strength. A relationship between penetration resistance and seedling emergence would serve as a guide to ensure optimum soil condition for a desired plant density.

Apparatus and Accessories

Penetrometer, moisture box, trowel, electronic balance and oven.

Soil aggregates refer to a group of two or more primary particles which cohere to each other more strongly than to surrounding particles. Under field conditions, these units also cohere to some degree with neighboring aggregates and form larger aggregates. The aggregates form and break easily into smaller aggregates during tillage and by disruptive action of water and air. The aggregates maintaining their identity are those in which the cohesive forces among particles are greater than the disruptive forces. A quantitative characterization of aggregates is being done by determining aggregate stability and size distribution of aggregates. The aggregate stability refers to the resistance of soil aggregates to breakdown by water, air, and mechanical manipulations. Numerous methods have been proposed to determine size distribution and stability of soil aggregates (Kemper and Rosenau, 1986). The size distribution of wet and dry aggregates determines overall tilth, size of pores and susceptibility of aggregates to movement by water and wind.

Proportion of solid, liquid (water), and gas (air) components in a soil mass indicates physical conditions such as soil structure, porosity and water content. A well Structured soil may have a high pore volume whereas a compacted soil may have little pore-space for other phases to occupy. In general, soil solids may occupy 50 % of the soil volume in a well-aggregated fertile loam and the remaining volume may be occupied by liquid and gases interchangeably.

Bulk density to which a soil can be compacted is dependent on water content and force of compaction. Adsorbed water on soil particles acts as lubricant and every addition of water increases ease of compaction to progressively a greater density. However, this increase in density will occur only to a certain value of water content beyond that it decreases with further addition of water in soil. The moisture content at which maximum dry density is obtained for a limited and fixed effort is called as proctor moisture and the density at that point is known as maximum or proctor density”.

Principle

A soil at varying water contents, compacted with a specific amount of compaction energy, would result in a bulk density-water content relationship. The maximum bulk density obtained at the proctor water content represents 100 % compaction. The test can be performed in laboratory on various soils using Abbot’s compactor

Particle density of soil is the ratio of mass and volume of soil solids. Particle density of soil is an important physical characteristic because the interrelationships of porosity, bulk density and air space, and rates of sedimentation of particles in fluids depend on particle density.

Principle

The determination of particle density (ρ_p) requires the measurement of mass of the solid and its volume. The mass is obtained by weighing the oven- dry soil sample and volume by calculating from the mass and density of water displaced by the sample in a pycnometer. The pycnometer is a glass flask with a ground glass stopper having a capillary opening. The particle density can be calculated using the following equation:

Bulk density is the ratio of oven-dry mass of soil to its total volume before drying. Knowledge of bulk density is useful in calculating moisture content by volume, porosity using particle density, volume of a known weight of soil, and weight of a furrow-slice. Bulk density is a direct measure of soil compaction. Bulk density is highly dependent on any kind of physical manipulation, degree of wetness, and kind and arrangement of soil particles. For this reason, preservation of original structure in the sample is most essential aspect of the methodology for determination of bulk density. Common methods of determining soil bulk density use soil cores or clods in their natural state.

Particle-size distribution is one of the stable soil characteristics, being little modified by agricultural management practices. The particle-size distribution expresses the proportion of various size particles in a soil, especially sand, silt and clay that determine soil texture (Hillel, 2004). Soil texture is closely related to soil physical properties such as water retention and release characteristics, surface area, swelling and shrinking, soil strength and tillage properties. There are two common methods being employed for particle-size (<2.0 mm) analysis namely international pipette method and the hydrometer method. Both the methods are based on Stokes’ law which establishes a relationship between particle size and the rate of sedimentation. Thus, particles are assessed by their settling velocities from suspension in a water solution that can be used to quantify particle size. Size range of soil particles (sand, silt and clay) according to two most adopted systems i.e. U.S. Department of Agriculture (USDA) and International Union of Soil Science (IUSS) formerly known as International Society of Soil Science (ISSS) are given below:

Precise collection and processing of soil sample is the first and foremost step for carrying out any soil study, therefore it is essential to collect the coil sample very accurately keeping in view the variations in slope, colour and texture of the soil management aspects. Soil sampling should be avoided from non-representative sites like recently fertilized plots, bunds, channels, marshy tracts and spots near trees, wells, compost piles etc. It should be kept in mind that erroneous samples will never yield accurate result in spite of very improved method of analysis. The depth of sampling may vary according to the need of soil study and extent of rhizosphere. However, in general the sampling depth for field crops is the plough layer (0 - 15 cm) but it could be up to one meter or as per the purpose of the study. Soil samples in dry farming areas should be taken depth wise as the roots of the crops in this conditions reaches to deeper layers in search of water and nutrients.

Mainly two types of soil samples (disturbed and undisturbed) are used for determining the physical properties of soil. The disturbed soil samples are used for determination of particle size distribution, particle density, aggregation, column studies for diffusion-dispersion coefficients and soil-water diffusivity. Whereas, undisturbed (core) soil samples are used for the estimation of bulk density, porosity, retention and transmission characteristics of air and water.

Maps make it easier to interpret and communicate the results of any GIS analysis. R has a large number of packages for making thematic maps. Static mapping is straightforward with plot()methods provided by core spatial packages sf and raster. The use of plot() function have been discussed in the previous chapters. Another package for mapping isspwithspplot() having enough options for producing good quality thematic maps.It is also possible to create advanced maps using ggplot2. This chapter discusses, separately, preparing thematic maps from vectors and rasters with the above mentioned packages. A dedicated map-making package tmaphas been given special focus in this chapter.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Supervised classification is the technique to classify raster image in which prior information about the classes in the form of training sites is provided by the user. Training site represents a homogeneous area in a particular class. The spectral response pattern of training sites is used to classify the raster image.The pixels of raster imagewhich have spectral response pattern similar to that of training sites of a particular class are grouped in that class. As there is some variability in the spectral response pattern of a particular class, therefore, multiple training sites per class are taken for supervised classification. Supervised classification of raster images can be performed by various methods. Conventional methods include maximum likelihood classifier, minimum distance to mean classifier, and parallelepiped classifier. Now a days, machine learning algorithms are also gaining importance in this context. These classifying algorithms are first trained or calibrated using the data of training sites.This trained model is then validated on the independent datasets for accuracy assessment. If the accuracy of the developed model is of acceptable level, then that model is used for the classification of raster data.

In this chapter, we are going to discuss the execution of maximum likelihood classifier and few machine learning techniques for supervised classification of raster image in R software. The training sites used in this exercise are in the form of points shapefile. The raster image is classified into six classesi.e. built- up, fallow soil, riverine sand, salt-affected soil, vegetation, and water. For performing this exercise, first of all,set the working directory and import the required packages.

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Raster transformations involve mathematical manipulation of raster layers to generate new raster images. These transformed raster images either contain the information of interest or highlight the features which are not discernible in the original image. Transformations are also performed to preserve the essential information in reduced number of dimensions or layers. In this chapter, we are going to discuss few most commonly used transformation tools listed below:

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

Sensors onboard the satellite records the intensity of emitted or reflected electromagnetic radiations in the form of digital number (DN) for each pixel. These digital numbers can be transformed to more meaningful intensity units like radiance or reflectance. This process of conversion of digital numbers to radiance or reflectance is known as radiometric calibration. Sensor specific information is required to conduct radiometric calibration.

The satellite data is available in raster format. Raster data consists of two – dimensional array of pixels or grids which stores the values representing features. All the pixels are of same size and shape with their particular coordinate location. A raster data format is shown below.

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

Maize (Zea mays L.) has tropical origin and is traditionally grown in monsoon. The crop is grown in diverse geographical and climate conditions around the world and in India maize is grown in kharif (rainy season), rabi (winter season) and spring seasons where more than 70 per cent of the production happen in kharif alone. Of late, the area under rabi maize has increased because of higher productivity and ever increasing demand.

Insect pests of maize

Compared to most cereals, maize faces fewer biotic and abiotic constraints in production. Among biotic stresses, insect pests are major constraints in the production and productivity of maize crop. The earlier literature cites over 160 insect and mite species which attack maize crop (Fletcher, 1914, 1917; Ayyar, 1940; Bhutani, 1961; Pant and Kalode, 1964) but later Mathur (1987) observed over 250 species of pests associated with maize in field and storage conditions. Dick and Guthrie (1988) identified 87 species that directly or indirectly exert severe stress on corn culture in tropical and temperate regions throughout the world. Excluding stored grain insects, Luckman (1978) lists 34 pests or pest groups for which chemical controls are recommended on corn in the United States. There have been more than 130 insect pests reported to cause damage to maize in India but only about a dozen cause economic losses (Sarup et al., 1987).

Shrinking land resources, vulnerable market of cash crops along with climatic aberrations have adversely affected the Indian agriculture and as a result, small farmers are increasingly alienating from this profession. To sustain their livelihood, farmers are increasingly adopting the alternative sources and dairy farming is one of the best-suited businesses, which can rightly fit at this stage. It is fast emerging as a potential business in rural India, particularly in the peri-urban belt. Among the major inputs required to set up a dairy unit, round the years availability of good quality fodder is the foremost requirement. Green forages are rich and cheapest source of carbohydrates, protein, vitamins and minerals for dairy animals. The importance of forages in our country is well recognized since feeding forages alone accounts for over 70% of the cost of milk production. Hence by providing sufficient quantities of green fodder instead of costly concentrates and feeds to the milch animals, the economics of dairy unit can easily be maintained. Milch animals need around 40 kg of green fodder daily for better milk production and for maintaining good health of the animal. There is a large number of fodder crops grown in our country, the majority being dominated by pearl millet, sorghum, maize and oats in the non-leguminous and berseem and lucerne in the leguminous category. Pearl millet and sorghum are extensively grown as cereals in the southern and dryer regions of the country and the stover remained is used as animal fodder. However, the nutritional quality of the stover is very poor due to higher content of indigestible lignin and low levels of cellulose, whereas, the cultivated green fodders are mostly confined to the North-Western plain zones of Punjab, Haryana and Western Uttar Pradesh. The availability of round the years quality green fodder, therefore, seems to be a tough task. At present, the country faces a net deficit of 61.1% green fodder, 21.9% dry crop residues and 64% feeds (IGFRI Vision, 2030).

Foodgrains are a fine example of diversity of nature with many varieties and varied features. They are storable food commodities and lend themselves to numerous processing manipulations. Nutritionally foodgrains are rich source of almost all known nutrients and several phyto-chemicals. The increasing incidence of metabolic diseases are attributed to unbalanced energy rich diets lacking in fiber and protective bioactive compounds such as micronutrients and phyto-chemicals (Prakash, 2013). Cereals along with pulse combinations constitute an important part of human diet in many parts of the world because of easy availability, low cost, long shelf life and nutritional balance. During recent past, India has made significant progress in improving the food security of its masses. The green revolution of 60’s helped the country in achieving the food security by improving the availability and access components (Kaul et al., 2016). However, surplus quantity of food produced is not uniformly distributed and the quality component is not addressed properly which has a direct impact on nutrition. In spite of surplus production of foodgrains still malnutrition persists. However, it can suitably be overcome by combining cereals with good quality pulses and other value addition techniques which will go long way in solving the problem of malnutrition. Even though country is producing large quantity of coarse grains particularly maize but its utilization for direct consumption is very low which is mainly due to poor quality of protein. Although maize contains higher proportion of protein (10-12 %), its quality is poor as compared to that of protein in rice due to presence of high concentration of alcohol soluble protein fraction ‘prolamine’ also known as ‘Zein’ in the endosperm. Zein is very low in some essential amino acids, mainly lysine and tryptophan which contribute to more than 50% of the total protein. On the other hand, very high amount of leucine and imbalanced proportion of isoleucine contribute to poor quality of protein in normal maize. To overcome such deficiencies, scientists developed Quality Protein Maize (QPM) where the quality of protein is not only higher than the common maize, but also significantly higher than that of other cereal grains. In the recent past QPM has got special distinction among the cereals due to presence of high amount of essential amino acids like lysine and tryptophan. QPM can be utilized for diversified purposes in food and nutritional security as infant food, health food, convenience food, specialty food and emergency ration. In India maize is consumed mainly in the form of boiled corn or roasted as pop corn. In order to increase the proportion of maize for direct consumption, several traditional food-processing and preparation methods can be used at household level to enhance the bioavailability of micronutrients in maize based diets. These methods include alkali cooking, thermal processing, mechanical processing, soaking, germination/ malting and fermentation. In countries like Central America and Africa, it is converted into food products by grinding, alkali processing, boiling, cooking and fermentation (Srilakshmi, 2008).

Maize or corn (Zea mays L.), which was first domesticated in Mexico, is the most widely grown cereal crop in the world. Maize is consumed as feed and food, and also has myriads industrial applications, including the production of bioethanol and starch. Due to its highest genetic potential, production and productivity among the cereal crops, maize is called as “Queen of the Cereals”. Noble Laureate Dr. Norman E. Borlaug, the Father of Green revolution stated 15 years ago that “The past two decades saw the revolution in rice and wheat, the next few decades will be known as maize era”. In India, maize is the third largest grain crop and its production and demand is increasing rapidly. In India, it is primarily a non-food cereal crop, with only 10% being consumed as direct human food, while the rest being used for feed, industrial and export purposes. The effective deployment of various frontier technologies viz. genetic engineering, doubled haploids, molecular markers, phenomics etc.has greatly helped in raising productivity of maize crop worldwide in the last decade.The present chapter provides an overview on role of frontier technologies in maize crop improvement.

Seed in agriculture recognized as one of the most vital and crucial input in agriculture ever since the human kind has started tilling the land to produce the food for his existences. Seed technologist defined seed as matured fertilized ovule consisting of an intact embryo, endosperm and or cotyledon with protective covering (seed coat). A quality hybrid maize seed production system ensures design of the production technology which enables to maintain the healthy embryo, endosperm and /or cotyledon with protective covering. In fact the search of quality seed had acted as a major force to evolve the science of plant breeding. Ever since the farmers understood the importance of seed, they started trading the quality seed within community and now between geographies. Self-pollinated crops possess the natural mechanisms to maintain the intactness of genetic constitution of a seed during multiplication as there is very minimal chance of natural crossings. On the contrary, cross pollinated crops like maize requires special techniques in seed multiplication to maintain the genetic constitution of a F₁ hybrid. Hybrid Maize seed production techniques are well defined and are used by public and private sectors to produce the required hybrid seed in India. In this chapter, I will be highlighting the process, potential agro-climatic zones, hybrid maize seed production capacity and its future perspective in India. In addition, it also reviews the current system and possible strategic move in order to support the maize seed production system and steps required to meet future demand.

Maize is a miracle crop and is popularly known as “Queen of Cereals” due to its very high yield potential. It’s a leading crop worldwide in terms of area, production and productivity and is suitable to diverse agro-climates. Its extent of adaptability is unmatched by any other crop. Due to this adaptability to diverse climates, maize had been grown by many generations in different parts of the world. Earlier, the primary utilisation of maize was focused towards human consumption and even today it is a staple food of many underprivileged societies especially in African countries and certain tribal pockets in India. It is playing an important role as a cheap source of carbohydrate in daily meals due to high productivity and wider adaptability. With the passage of time the productivity increased due to conscious selection towards high yield by farmers and researchers. This enhanced production and productivity opened new doors of utilisation of this wonderful crop. Maize found its place as a feed crop for economically important anima ls like chicken and swine. This shift towards feed industry got encouragement by change in the food habits of people. Looking at the recent consumption pattern of maize as a food crop displays declining trend. As per a NSSO survey (2014) maize consumption in rural areas in India has seen a decline of around 35% from 3.7 kg/year in 2004-05 to 2.4 kg per year in 2009-10, and further declined to 1.56 kg during the year 2011-12. Continued efforts towards yield enhancement and decline in human consumption made its way to utilise surplus maize in economically viable industrial products. Maize is a crop where every part of its plant can be utilised to produce food and non-food products. Oil, starch, glucose, dextrose and sorbitol are few important products produced by processing of maize grains. Apart from these, maize has been very widely used for production of ethanol which is used as a fuel in many developed countries. Direct utilisation of maize grains in poultry and other feed industries is on the rise with increasing demand of such foods. Maize oil is of high quality and is being widely used in developed countries. Maize is a major source of industrial starch which finds its way to diverse utilization such as food and beverage, pharmaceutical, textile, paper and other industries. Processing of maize is done by two ways- Dry Milling and Wet Milling. Wet milling produces starch, gluten and husk and Dry Milling produces corn meal, germ, grits and animal feed. Apart from these processed products, maize is also being developed to be utilised as a biopharmaceutical crop. Maize crop is being bioengineered to extract products of therapeutic and medicinal importance. Such a diverse utilisation of maize has generated a necessity to improve morpho-physiological traits which are important with respect to its industrial utilisation. In the present chapter, we will discuss various aspects of maize improvement for industrially important traits which will include oil, starch, methionine, biofuel and biopharmaceuticals.