eChapters

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:

In conventional breeding for genetic upgradation of yield and other desirable attributes of crop plants, favourable assemblages of genes from two or more parents are made to develop a commercial variety. The important steps of this exercise are: locating the right kind of parents that possess the desired characteristics; making crosses between or among them; handling the segregating generations in an appropriate manner so as to identify the required recombinants; stabilizing the recombinants and finally evaluating their performance in a series of trials. This approach has been very successful in the sense that it has provided increments in productivity continuously. In an analysis done by the American Society of Agronomy on the genetic contributions to yield gains of five major crop plants, it was concluded that sorghum yields increased due to genetic improvements at the rate of 1.2 per cent per year between 1950 and 1980. For corn, the corresponding yield increase was 90 kg/ha/year between 1930 and 1980, which is equivalent to a rate of increase of 1 per cent per year. It was also estimated that 70 per cent of the total gains in corn yields are attributable to genetic improvement and 30 per cent of the yield gains are due to non-seed inputs like fertilizers, pesticides and cultural practices.

In India also, the improvement of productivity of the major food, fibre and several cash crops has been very impressive. The success is due to intensive research in plant breeding resulting in the evolution of varieties with very high genetic yield potentials and the concurrent support from other disciplines that has provided the package of practices which help in the realization of the created potential. In all crop plants, where the breeding efforts have been intensive, the phenomenon of plateauing of yields has become apparent. Wheat provides, perhaps, the best example of the situation. Since the 1970s, when the gains from genetic restructuring of the plant type had been established, major yield increases have not occurred. Since the demands of the future will be undoubtedly much larger, and additional cultivable land will not be available, ways will have to be found for a sizable increase in productivity per unit area in the immediate future. This requirement calls for the development of strategies and procedures that will make dramatic yield gains possible. For meeting the challenge, a continuous search is being made for innovative methods which will give yield increments of a much higher order than those provided by the conventional approach. In this chapter, an attempt has been made to introduce some such promising approaches.

Genetic resources deal with germplasm of a given species that provides the necessary genetic variation for the improvement of that species in terms of particular products, increased efficiency and higher levels of productivity. Genetic resources are, in this sense, the building blocks, fundamental not only to a crop improvement programme, but also for the very survival of the species in time and space (Swaminathan, 1983).

As agriculture is becoming more and more intensive and location-specific, plant breeding objectives are becoming more and more complex. Besides high yield, a modern variety is often expected to combine specific duration, nutritional attributes, adaptation to varying soil and water regimes, and resistance to ever-multiplying pests and diseases—to mention only a few of the character combinations. To meet these objectives, a wide range of germplasm must exist, and the plant breeder should have an easy access to the genetic resources.

Since genetic variability is essential for any crop improvement programme, the creation and management of genetic variability becomes central to crop breeding. Experimentally induced mutations provide an important source of variability. An advantage of such created variability is that the starting point can be an agronomically accepted cultivar rather than a genetic stock of no direct commercial value with many undesirable attributes. It should, however, be realized that none of the imposed treatments in a plant system has so far been able to direct a specific change. As a result, following mutagenic treatments, a mixed bag of induced variants is found and many of these may not be of any value. Also, many events take place concurrently in a treated cell to produce variation at several loci within a genome. Because of these reasons it will be unrealistic to expect miracles out of a mutation breeding programme. The desired results can be achieved only when the objectives are clearly defined and the experiments are specifically designed to achieve those objectives. When a mutant of the required type is obtained, we only reach the step of identification of a desired recombinant in a conventional breeding programme and all the succeeding steps of evaluation of the mutant are in no way different from those in the conventional breeding procedures.

Breeding methods for vegetable crops are basically similar to those adopted in others like cereals, oilseeds or pulses. Certain aspects, peculiar to vegetables, however, require specific attention: (i) In many cases, the edible part of the plant is not the seed (as cauliflower, cabbage, carrot and onion) and hence while breeding for improved plant part that is used as vegetable, its seed production capacity needs due consideration, (ii) Vegetables like tomato, garden pea and onion are consumed in different forms (fresh or processed) and the end use of the product (like processing quality) has to be taken into account in breeding programmes, (iii) Consumer preferences (as for example in eggplant) size, colour and shape of the vegetable has to be given due emphasis, (iv) Being perishable in nature, keeping quality and suitability for long distance transportation are important attributes in vegetable crops unlike grain crops where bulk handling and haulage can be mechanised, (v) Another peculiarity with vegetable crops is that they vary widely in modes of reproduction necessitating appropriate choice of breeding methods. In addition to vegetatively propagated crops like potato, sweet potato, cassava (tapioca), garlic, pointed gourd or “Parwal (Trichosanthes dioica L.) and ivy gourd or ‘Kundru’ (Coccinia cordifolia L.), there are many seed propagated vegetable crops which are auto- or allogamous or intermediate (Table 1).

Within these broad groups, variations in the extent of cross- or self-pollination, namely, 15-20 per cent natural cross-pollination in chillies and 20-25 per cent natural self-pollination in muskmelon have been recorded.

Forage crops are plant species used to feed domestic animals (such as cattle, sheep, goat, pig, and poultry). Both cultivated and wild plants can serve as forage. The domestic animals feed on forages either by grazing or stall feeding (cut fodder eaten in stalls).

Grazing, that is pasturing of animals, is cheaper and allows production of both livestock and dairy produce at a lower cost because no expense is involved in growing, cutting, transporting or feeding. In North and South America, Australia, New Zealand, and in many other temperate countries pastures are widespread and popular because sufficient land is available. While land is cheap and plentiful, labour is scarce and expensive; hence management of natural, and in recent years sown pastures has received much attention.

Until about fifty years ago, the natural grasslands in the Western countries met the minimum needs of the farmers and grassland management was operated at a level comparable to subsistence agriculture of many developing countries. In other words, emphasis was laid on exploitation of natural productivity of the grassland, using a low level of management based on minimum use of inputs like chemical fertilizers. However, it was soon realized that productivity of native grasslands will have to be greatly improved if the high demands for meat and milk were to be adequately met.

In India, however, out of a total cultivable area of about 131.11 million ha less than 2 per cent is utilized for forage crops because feeding the large human population takes the first preference for land use to produce grain and cash crops. It is, therefore, necessary to intensify efforts, particularly in over-populated countries, for a more efficient production of high yielding forage crops to get maximum return out of minimum land.

Introduction

Tree improvement through the application of genetic principles is directed towards modifying the heredity of tree populations so that they are able to meet the needs of the forester and the wood based industry. The breeder of annual crop plants resorts to hybridisation, selection, testing of the improved strains, multiplication of improved seeds and finally their release for commercial planting in that logical sequence. But the tree breeder, whose career may be shorter than the life cycle of the tree species taken up for improvement, tends to carry out the above operations in a modified form and often some of the operations are carried out concurrently.

Many of our tree species are wild unselected populations. They are, therefore, expected to exhibit considerable natural variability for many characters and also to respond well to selection for further improvement. Further, altitudinal and latitudinal variation in many characters between populations are expected for those tree species which have a wide geographical distribution. So, the selection of desirable seed sources or provenances is an essential step that is basic to all tree improvement programmes. The right provenance is identified through well laid out provenance trials at a number of locations in the area where the improved selection is to be grown. Further improvement is then planned and executed for exploiting the naturally available intrapopulation variation. The variation normally measured is the phenotypic variation which is the result of interaction of the genotype of the individual with the environment. However, what is essential for selection to be effective is genetic variation for the character under consideration. Genetic variation is assessed by appropriate progeny trials. Since many of the economically valuable characters of tree species are polygenically controlled and exbibit continuous variation, measurements have to be in metric units. The use of biometrical techniques for assessing and analysing this variation, therefore, becomes an essential requirement.

Most varieties of fruit plants have originated as chance seedlings or through somatic mutation and have been maintained and multiplied through vegetative propagation by fruit growers. The improvement of fruit plants through breeding has not yet attracted the serious attention of the research workers. Unlike field crops, fruit plant breeding is time consuming and laborious. Studies on the mode of inheritance of important economic characters of fruit plants are difficult because of the heterozygous nature of the plants. Moreover, varying degrees of sexual incompatibility have complicated the situation. Due to a long juvenile phase, it takes a long time to identify a useful hybrid. This factor has become a serious handicap because most research workers leave or shift from the programme before tangible results are obtained.

During the last two decades, considerable useful information has been generated on breeding methodology for the improvement of some of the important fruit crops. In this chapter fruits like mango, banana and papaya have been included.

The immediate and long-term objective of modern plant breeding in all countries irrespective of the state of their advancement is increasing the availability of food to meet the calorie-protein requirement of their people. In the past, breeders have concentrated their efforts on increasing the yield of the important crop species. Better adaptation to local environmental conditions, high production and better resistance to the major pests and diseases have been among the priority goals of genetic plant improvement. Yielding ability and stability undoubtedly deserve high priority but enough attention also needs to be paid to improving nutritional characteristics of the plant produce for their maximum utilisation. Awareness of nutritional requirements and the optimal characteristics of crop products to be utilised as food and feed, therefore, becomes an important parameter of breeding effort.

The main source of food in the developing world is cereals which provides 45 to 85 per cent of the total calories and 50 to 80 per cent of the total protein requirements. The consumption of animal proteins, which are nutritionally well balanced, is low and costly and beyond the reach of the low income groups. On the other hand, plant proteins are cheaper to produce and easier to store and transport. All these factors go in favour of increasing the quantity and quality of cereal proteins in order to improve the dietary and nutritional status of the masses in the developing world.

Proteins are made up of about twenty different amino acids. For man, eight of these amino acids, valine, methionine, leucine, isoleucine, threonine, phenylalanine, lysine and tryptophan, are essential (EAA) for constituting a well balanced diet. In impioving the grain quality of cereals consideration should, therefore, be given apart from the content of carbohydrates, fats, minerals and vitamins in the grains, to improving the protein content as also the amino acid composition of the protein.

Plant diseases, in spite of the various measures adopted to control them, continue to be a major cause of crop losses. At times these losses have been so heavy that they have caused severe famines. Diseases are caused by a wide variety of organisms such as fungi, bacteria, viruses, mycoplasma and nematodes. The diseases may be partially or completely controlled by chemical, cultural and genetic methods. When no single method is effective in controlling a disease, integrated control constituting a combination of the three methods of disease control (chemical, cultural, genetic) may be effective. If host resistance alone is sufficient to check a disease, then it is to be preferred to other methods because it is the least expensive, has no adverse environmental effects and involves no specific action on the part of the farmer, even when only partial, genetic resistance supplements chemical control and makes it more effective in comparison with chemical control on a completely susceptible cultivar.

Many plant diseases have been successfully controlled by exploiting genetic resistance especially where differentiation in the pathogen with respect to resistance genes in the host is lacking. In some cases, even when pathotype differentiation occurs in the pathogen, genetic resistance in combination with other methods has given adequate control. Breeding for resistance to highly variable air-borne fungal pathogens has claimed much time and effort of plant breeders and success has often been transient. The reasons for the shortlived success have become clear from research during the last quarter of a century.

A close relationship between the mode of reproduction of a cultivated plant and the methods to be employed for its improvement is now well accepted. Crop plants are broadly placed in two groups according to the mode of their reproduction: sexually reproduced and asexually propagated or reproduced plants. Asexual reproduction does not involve the union of gametes and/or reduction in chromosome numbers.

Plants may reproduce asexually in a variety of ways. In 1980, Mayo has identified the following methods of asexual reproduction on the basis of the classification made earlier by Stebbins in 1941 and Brown in 1972.

1) Origin and distribution

Pearl millet (Pennisetum americanum (L) Leeke, 2n-14) originated in west Africa from where it was introduced into India. Large variability is still found in Africa. Pearl millet is a dual purpose crop; its grain is used for human consumption and its fodder as cattle feed. It is cultivated mainly in Africa and Asia, primarily as a grain crop in an area of 26 million ha with a production of 13 million tonnes. As a poor man’s bread, it sustains a large population of Africa and Asia. It also contributes to the economy of countries like the U.S.A. where it is grown as a forage crop.

In India, Pennisetum americanum (bajra) is cultivated on 11 to 12 million ha land with a production of about 6 million tonnes. It is grown on marginal lands and in areas of scanty rainfall. Rajasthan, Gujarat, Maharashtra, Uttar Pradesh and Haryana account for about 90 per cent of the total area under this crop.

Cross-pollinated species, or outbreeders, are highly heterozygous, heterogeneous and carry a higher mutation load than the inbreeders. Their mode of reproduction bestows upon them considerable heterozygous balance under panmitic population structure. Inbreeding such species disturbs the natural rhythm and leads to inbreeding depression. It is, therefore, essential that if selfing has created any disturbance in outbreeders, the heterozygous structure be restored. Since artificial selfing and outcrossing can be carried out in such crops at will, all breeding methodologies, even those meant for self-pollinated crops, can be used for their improvement. In fact, allogamous crops have provided opportunities for innovating breeding procedures to achieve the maximum efficiency by combining selfing and outcrossing cycles.

Thus, the classical separation of breeding procedures for self- and cross-pollinated crops has become unnecessary, particularly, for outbreeders which are amenable to selfing. Exploitation of heterosis has been the major objective in cross-pollinated crops and most breeding procedures developed earlier aimed at maximising heterosis. In later years, with better understanding of genetic structure of populations and availability of efficient biometrical methods of estimating genetic parameters, newer procedures of accumulating desirable genes have been developed. The presently available breeding procedures for cross-pollinated crops can be broadly classified under two heads: (1) Hybrid development, and (2) Population improvement. Both these methodologies, though independent, are not mutually exclusive. The steps for operating the two procedures, however, have contrasting requirements. For hybrid development, fixation of genes through cycles of selfing is necessary whereas such fixation is avoided while developing populations. As can be seen from the scheme given below, in both cases the homozygosity attained through initial selfing is converted to heterozygosity at the end of breeding programme. Irrespective of whether hybrids, synthetics or composites are being developed, great emphasis is placed on the attainment of homogeneity in the end products.

Man-directed evolution of distinct types and cultivars (cultivated populations) of self-fertilised crops had their beginning about 10,000 to 12,000 years ago. As a breeding system, self-fertilisation favoured constancy and fitness (Stebbins, 1950) of the genotypes to the immediate environment. This provided an alternative to the slash and burn migrant agriculture of the Neolithic farmers (7,000-6,000 B.C.) and to stabilisation of production.

Manu Smriti (700–500 B.C.) proclaimed Subeejam Sukshetre Jayate Sampadyate meaning that good seed in a good field yields abundant produce. The knowledge that good seed is essential for a good harvest, led to conscious efforts for selecting types which provided a dependable source of food. Besides their utility as food, choice of plants and their seeds as progenitors of the next generation depended on personal fancy, observation and experience. As farmers learnt their crop plants better, they sorted them out into similar types (species). While migrating from the centres of origin they carried seeds of mixed populations.

Self-pollinated cereal crops of barley, wheat, oats and important grain legumes such as lentil, peas and chickpea spread from the Near East Centre, first to far and remote areas of Asia, Europe and Africa; and subsequently in comparatively recent times, to the Americas and Australia. In new environments, the introduced mixed populations diversified into well adapted identities of land races. These land races continued to be the main basis of cereal production till early 1900. Improvement in crop plants made by some early civilisations was mainly through introduction and mass selection, based on the natural tendency of preserving the seeds of preferred colour and size from vigorous, healthy and more productive plants.

It was only after the rediscovery of Mendel’s laws in 1900 that breeding procedures based on the knowledge of the genetic principles and the inheritance pattern of different characteristics were developed. This permitted the improvement of crops with objectivity and predictability.

Plant breeding is essentially a technology. Any technology will stand the test of time if it is based on sound concepts. In the case of plant breeding, the concepts are provided by the science of genetics.

Plant productivity in its broad sense requires consideration of not only yield but also of a number of its direct and indirect components. For example in groundnut, besides pod yield, shelling percentage, 100-kernel weight and oil content are components relevant to defining productivity. A number of physiological (like biomass, leaf area, specific leaf weight), morphological (like days to flowering, number of branches, number of mature seeds), nitrogen-fixing, (where relevant, for example, number of nodules, nodule mass, nitrogenase activity) and such other characters influence yield directly or indirectly.

Yield and most yield contributing characters are quantitative in nature. A quantitative character is one which shows continuous variation. In contrast, qualitative characters like flower colour and seed coat colour show, in general, discrete variation and are controlled by fewer genes with major effects. A quantitative trait, however, is governed by many genes. The range of values of a quantitative character is described by a continuous distribution, usually assumed to be a normal distribution. The distribution is specified by the two parameters: mean and variance.

NTSYSpc 2.0 software is used for various types of analysis including calculation of similarity index values and construction of dendrogram. Construction of dendrogram using gel data in the form of bands. This software accepts zero-one data.

Metagenomics is a relatively new field that combines molecular biology and genetics in an attempt to identify, and characterize the genetic material from environmental samples and apply that knowledge. The genetic diversity is assessed by isolation of DNA followed by direct cloning of functional genes from the environmental sample. But more importantly, it is extremely beneficial in the study of synthetically made genomes. Metagenomics is described as “the comprehensive study of nucleotide sequence, structure, regulation, and function”. Scientists can study the smallest component of an environmental system by extracting DNA from organisms in the system and inserting it into a synthetic model organism. The model organism then expresses this DNA where it can be studied using standard laboratory techniques. The development of metagenomics stemmed from the ineluctable evidence that as-yet-uncultured microorganisms represent thevast majority of organisms in most environments on earth. This evidence was derived from analyses of 16S rRNA gene sequences amplified directly from the environment, an approach that avoided the bias imposed by culturing and led to the discovery of vast new lineages of microbial life.

Metagenomics is employed as a means of systematically investigating, classifying, and manipulating the entire genetic material isolated from environmental samples. This is a multi-step process that relies on the efficiency of four general steps as shown in the diagram. The procedure consists of: the isolation of genetic material, the manipulation of the genetic material, library construction, and the analysis of genetic material in the metagenomic library. Metagenomics provides a second tier of technical innovation that facilitates study of the physiology and ecology of environmental microorganisms. Novel genes and gene products discovered through metagenomics include the first bacteriorhodopsin of bacterial origin; novel small molecules with antimicrobial activity; and new members of families of known proteins, such as an Na⁺(Li⁺)/H⁺ ant porter, RecA, DNA polymerase, and antibiotic resistance determinants. Reassemblyof multiple genomes has provided insight into energy and nutrient cycling within the community, genome structure, gene function, population genetics and micro heterogeneity, and lateral gene transfer among members of an uncultured community. The application of metagenomic sequence information will facilitate the design of better culturing strategies to link genomic analysis with pure culture studies.

Sanger Method for DNA Sequencing

One necessity first know a little about the structure of DNA:

1. A segment of DNA, which is ordinarily double stranded, has a specific orientation, as it has a 5' (read as “5 prime”) and a 3' (“3 prime”) end. This can be simply thought of as a front and tail end to the DNA segment.
2. When DNA is synthesized in the lab, the two strands are separated and new bases are added to the 3' end-thus DNA is assembled from the 5' to 3' end.
3. DNA cannot be synthesized from scratch. A short piece of DNA, called a primer, is required for the reaction to begin.
4. Primers are designed such that they are able to bind to the target DNA, the binding of which is the initiator for DNA synthesis.

Introduction

The northern blot is a technique used in molecular biology research to study gene expression by detection of RNA (or isolated mRNA) in a sample. With northern blotting it is possible to observe cellular control over structure and function by determining the particular gene expression levels during differentiation, morphogenesis, as well as abnormal or diseased conditions. Northern blotting involves the use of electrophoresis to separate RNA samples by size and detection with a hybridization probe complementary to part of or the entire target sequence. The term ‘northern blot’ actually refers specifically to the capillary transfer of RNA from the electrophoresis gel to the blotting membrane; however the entire process is commonly referred to as northern blotting. The northern blot technique was developed in 1977 by James Alwine, David Kemp, and George Stark at Stanford University. Northern blotting takes its name from its similarity to the first blotting technique, the Southern blot, named for biologist Edwin Southern. The major difference is that RNA, rather than DNA, is analyzed in the northern blot.

Introduction

Western blot analysis can detect one protein in a mixture of any number of proteins while giving you information about the size of the protein. It does not matter whether the protein has been synthesized in vivo or in vitro. This method is, however, dependent on the use of a high-quality antibody directed against a desired protein. So you must be able to produce at least a small portion of the protein from a cloned DNA fragment. You will use this antibody as a probe to detect the protein of interest.

Western blotting tells you how much protein has accumulated in cells. If you are interested in the rate of synthesis of a protein, Radio-Immune Precipitation (RIP) may be the best assay for you. Also, if a protein is degraded quickly, Western blotting won’t detect it well; you’ll need to use (RIP). See the section on RIP for more information, as well as a helpful comparative chart that illustrates the differences between these two techniques.

Limitations of Bacteria

• Normally genetically engineered bacteria are very useful for the large scale production of valuable eukaryotic proteins.
• Bacteria grow rapidly, They grow on relatively simple media, they are easy to handle, they are easily transformed, they can grow on wide range of substrates, they multiply, fast, they contain simple and well characterized genome, they can be easily modified but even though, they have their own problems…

1. Bacteria do not have intrudes and so they do not have any system or machinery to remove introns.
2. Many eukaryotic proteins undergo different types of modifications after they are produced. These modifications are known as “post translation al modifications”.
3. Each bacterial cell usually contains many copies of the plasmid but there is no mechanism to ensure the equal distribution of plasmids in daughter cells.

To isolate a streptomycin resistant mutant of E.coli by Indirect selection using Replica Plate Method.

Introduction

J. Lederberg & his wife E. M. Lederberg devised this technique in 1952.

U.V. light coming from the sun emits strong radiation to extent the antibacterial effect in the microbial population. However it has a very short penetration power, UV light is used as a mutagenic agent & was used by Beedle & Tatum to obtain biochemical mutation. The mutation rate is only once in 10^-9 cells per population. Then hence using mutagens like UV light by 10 to 1000 folds will increase this mutation rate.

In isolation of the mutant using UV light the dose of UV light is measured by its units/nm² organisms. The time selected for the mutation is calculated on the basis of 0.1 to 1% survival & a suitable biochemical character is used to evaluate the mutation.

To isolate a streptomycin resistant mutant of E.coli by Direct selection using Gradient Plate Method.

Introduction

Zylbalskii devised this technique.

A mutation is any change in the nucleotide sequence of DNA. These changes are heritable. Although most mutations are harmful to the organism, a small percentage is beneficial under certain environmental conditions. Mutation can occur during chromosome duplication and they may be spontaneous of they may be induced by mutagenic agent. In this experiment, we select for a mutation causing streptomycin resistance in E. coli. Resistant mutants will be detected directly using thy gradient plate method.

Principle

Microbiologist developed a highly efficient method for isolation of antibiotic resistant mutant. The gradient is said as gradient of antibiotic formed on an agar plate. A gene imparting resistant toward an antibiotic is present in plasmid or chromosome. The occurrence of mutant with different level of streptomycin resistant involves mutation of gene with unequal potential. This means that under certain cells degree of mutation is more while few shows less mutation. Some cells show intermediate resistant while others show completes resistant. The resistant in cell against streptomycin of this type, while in case of penicillin resistant increases step arise & so resistant mutant of high concentration of penicillin cannot be isolated/ obtained. While it is possible with streptomycin obtaining high resistance to penicillin stepwise & so this stepwise increase in resistance involves mutation of gene with equal potential. This theory of mutation through multi step procedure. The gene direct the organism to produce an enzyme, which inactivates an antibiotic & thus resistance to antibiotic, is observed in case of cells. Resistance of antibiotic is a plasmid-mediated resistance.

Introduction

The isolation of mutants is often critical to the study of cellular process, especially when such studies are performed at the molecular level. The phenotype of a particular mutant is attributable to the loss of a particular cellular function; the loss of that function is actually attributable to a mutation in a gene necessary for that function. In this experiment we will isolate auxotrophic mutant which can grow only if provided with specific amino acid. For example arg^- mutant have lost the catalytic function of an enzyme involved in arginine biosynthesis due to mutation in the structural gene that encodes the enzyme.

Mutagenic Agent

Mutation is an alteration in the coding sequence of a gene. Mutation occur spontaneously at a frequency of about 10^-8 per bacterial cell per generation. The rate of mutation can be increased with mutagenic agents. Mutants are strains with any structural alteration of their DNA that gives rise to a mutant phenotype. Mutagens are chemical agents (e.g. nitorsoguanidine, 5 bromouracil), physical (e.g.UV) and biological agents (e.g. trnasposons) that cause mutation to occur.

Introduction

Mutation refers to any heritable change in the base sequence of a DNA molecule. A mutagen is a physical of chemical agent that causes mutations to occur (or increase the frequency of their occurrence). Mutagenesis is the process of producing a mutation. If the mutation occurs in nature without the addition of a mutagen, it is referred as a spontaneous mutation, and the resulting mutants are spontaneous mutants. If it caused by a mutagen, the process is induced mutagenesis.

Ultraviolet light is a fairly potent mutagen. The main DNA lesions are two chemically different types of covalent dimers: cyclobutane-pyrimidine dimers and pyrimidine dimers. The pyrimidine dimers only account for 20% of the total dimers but are correspondingly more mutagenic. In E. coli the number of mutants induced by ultraviolet (UV) light can be reduced by exposure to visible light (photoreactivation), which implicates cyclobutane pyrimidine dimers since dimers can not be photoreactivated.

Aromatic amino acid of proteins, and purine as well as and pyrimidine bases of nucleic acid absorb ultra violet radiation. Pyridine bases, particularly thymine of DNA is the main target compound involved in bactericidal action of ultra violet radiation. The energy absorbed by thymine causes it to under go a photochemical reaction with adjacent ( adjoining) thymine molecule in same DNA strand. DNA replication is blocked because the formation of thymine: thymine (T:T) dimmers. As the dosage increase, cytosine-thymine (C:T) to (C:C) cytosine-cytosine dimmers can be found to add the lethality of ultra violate radiation.

Introduction

Genetic variability is essential for the evolutionary success of all organisms. In diploid eukaryotic the processes of crossing over exchange of genetic material between homologous chromosomes and meiosis contributes to his variability. In haploid, asexually reproducing prokaryotic organisms, genetic recombination may occur by conjugation transduction, and transformation. In this experiment only the process of conjugation is considered.

Conjugation is a matting process during which a one direction transfer of genetic material occurs at physical contact between two sexually differentiated cell types. This differentiation or existence of different mating strains in some bacteria, is determined by the presence of a fertility factor of F factor, within the cell. Cells that lack the F factor are recipients (females) of the genetic material during conjugation and are designated as F^- . cells possessing the F factor have ability to act as genetic donors (males)during the mating. If this F factor is extra chromosomal (a Plasmid or episome), the cells are designated as F⁺; most commonly only the F factor is transferred during conjugation. If this factor becomes incorporated into the bacterial chromosome there is a transfer of chromosomal genes, although generally not involving the entire chromosome or the F factor. The resulting cells are designated as Hfr, for high frequency recombinants.

Introduction

The most common method of bacterial transformation utilizes high levels of calcium chloride to facilitate the entry of plasmid DNA vectors into E.coli in conjunction with structural alteration of the bacterial cell wall. In general, there are three basic steps in the introduction of plasmid DNA unto cells.

1. Preparation of “competent” cells to enable cells to accept DNA.
2. Transformation of these competent cells, and
3. Selection of transformants.

Most E.coli strains exhibit transformation efficiency (measured as transformants per microgram plasmid DNA) in the range of 10⁵ to 10⁸.

The factors influencing this efficiency depends upon the condition that render cells competent.

To Prepare the Competent Cell and Carry Out Transformation Introduction

Transformation results from the uptake of purified DNA by bacterial cells and is the most frequently used procedure for introducing recombinant DNA molecules into bacteria. It is now widely used to transfer small plasmids from one bacterial strain to another. DNA is extracted from donor cell and used to treat the recipient cells that have been rendered more susceptible to DNA uptake. These competent cells allow DNA to enter through pores or channels in the cell membrane and in the case of plasmids, permit subsequent plasmid replication.