eChapters

Seed enhancement is the process or technology to further improve seed performances under very specific regimes and with certain planting equipment. Three major seed enhancement technologies are as follows (Copeland and McDonald, 1995). 
        Seed hydration 
        Biological seed treatments 
        Seed coatings

After harvesting and threshing, the seed usually arrives at the cleaning plant with trash, green leaves, weeds, other crop seeds and insects, etc. If the seed contains leaves, etc. and other high moisture materials, it cannot be accurately cleaned and safely stored until most of the foreign material has been removed. The process of removing the unwanted materials from a seed lot is known as seed conditioning. Seed conditioning is an essential component of seed technology to ensure that the farmers get the seed of high quality.

Seed quality is a multiple criterion that encompasses several important attributes. A seed scientist may be concerned with each quality characteristic of an individual seed while the seed trader normally considers the quality components of a seed lot. Any individual seed may possess certain measurable seed quality components which include:

Seed certification is a legal programme to maintain and make available to the public high quality seeds and planting material of genetically distinct crop varieties. Under seed production programme, quality seed is produced by govt. farms research stations, progressive farmers and seed producers using careful quality control, pedigreed plant stock, field inspections during the growing season and seed inspections after the harvest. Certification is an officially recognized method for maintaining varietal identity of the seed. In India certification is carried out by State Seed Certification Agencies.

Certified seed must meet certain seed standards in order to ensure an adequate level of seed purity along with the reasonable level of seed germination so that the farmer gets a good stand of the crop conforming to the varietal description. The components of the seed standard are as follows :
     The minimum percentage of pure seed 
     The maximum permissible limit for inert matter 
     Other crops seeds  
     Weed seeds 
     The maximum permissible limit for objectionable weeds 
     The maximum permissible limits of seeds of other distinguishable varieties (ODV) 
     Maximum permissible limits of seeds infected by seed bone diseases 
These standards are ensured in the seed crop by strictly adhering to field standards and careful harvesting, threshing, seed drying and then brining the same to the seed processing /seed conditioning plants. The Indian minimum seed standards for different crops are given in Tables 24.1-24.3 (Agarwal, 1995).

Minimum field standards for seed production have been prescribed  for each crop. The major components to be looked into for meeting the field standards are: 
   Preceding crops 
   Isolation distance 
   Number of field inspections 

The minimum proceding crop requirement has been specified to minimize genetic contamination on account of volunteer plants germinating from the left over seeds in the previous season. The disease contamination is also possible from soil borne disease organisms if the previous crop was not certified. The preceding crop requirement would mean that for a prescribed minimum period the same crop or any other contaminating crop should not have been grown on the same field where seed production is to be taken up unless the preceding crop was a seed crop and had met the field/the seed standards.

Indian seed market is one of the largest in the world and entire supply is through almost locally produced seed. The value of currently marketed seed (excluding the major share of seed saved and used by the farmers) is about USD1 billion (Rs.4500/crore). Of this, roughly a third includes the traded seed making the seed business worth about Rs. 1500 crore. This figure is likely to touch Rs. 4500 crore (USD 1 billion) crore mark by 2010. In comparison to about USD 1 billion seed business in India, the worldwide market of seed is worth about USD 21 billion. The global pesticide market is worth USD 35 billion. These values, however, are much smaller in comparison to global sale of pharmaceuticals worth USD 466 billion.

Issue

2004-2005 saw an upsurge in seed industry takeovers and a shake-up in rankings. Today, the top 10 companies control half of the world’s commercial seed sales. With a total worldwide market of approximately US$21,000 million [$21 billion] per annum, the commercial seed industry is relatively small compared to the global pesticide market ($35,400 million), and it is positively puny compared to pharmaceutical sales ($466,000 million). But corporate control and ownership of seeds – the first link in the food chain – has far-reaching implications for global food security. This chapter examines seed industry consolidation and other recent trends in the commercial seed industry.

Seed is the most important component for productive agriculture. Significant advances have been made in production of food crops in India during the last four decades. The quantum jump in the food production primarily accounted for by increased productivity and production in rice and wheat was the result of introduction of improved varieties and their seeds often nicknamed as Green Revolution. The food production which was nearly 50 million tones during 1950 – 51 reached to a level of 210 million tones during 2003 – 04. During the same period the productivity increased about 3 folds (Table 20.1).

No country is self-sufficient in plant genetic resources, and until recently this was thought to be sufficient to ensure the free flow of germplasm among nations. However, the concepts of ownership, sovereignty and intellectual property rights (IPR) have increasingly been invoked of late in discussions of how best to conserve and use plant genetic resources. This has resulted from the growing realization that germplasm has real economic value. At the same time, awareness has grown that the repercussions of overexploitation of natural resources, of the extinction of species and the disappearance of crop landraces, of environmental damage and of habitat destruction can be global, transcending national boundaries and short-term financial considerations (Anon. 1995). Therefore important international agreements/legislations on plant genetic resources management have been enacted from time to time. The important ones are as follows :

Plant quarantine is a government endeavor enforced through legislative measures to regulate the introduction of planting materials, plant products, soil, living organisms, etc., in order to prevent inadvertent introduction of insect-pests, pathogens and weeds harmful for the agriculture of a country/state/region, and if introduced, prevent their establishment and further spread. Various methods of pest disease control are: exclusion, eradication, protection, therapy, resistance, and biological control. Exclusion or ‘keeping out’ is fundamental to the concept of plant quarantine while eradication methods are employed to eliminate a newly established pest/pathogen. Plant quarantine may, therefore, be defined as ‘Rules and regulations promulgated by governments to regulate the introduction of plants, planting materials, plant products, soil living organisms, etc. with a view to prevent inadvertent introduction of exotic pests, weeds and pathogens harmful to the agriculture or the environment of a country/region, and if introduced, to prevent their establishment and further spread’. Plant quarantine is designed as a safeguard against harmful pests/pathogens exotic to a country or a region (Ram Nath, 1991, Khetrapal et al., 2004).

International Board for Plant Genetic Resources (IBPGR) now known as International Plant Genetic Resources Institute (IPGRI) uses the following items in genetic resources documentation. 
    i)    Passport (accession identifiers and information recorded by collectors) 
    ii)    Characterization (consists of recording those characters which are highly heritable, can be easily seen by the eye and are expressed in all environments) 
    iii)    Preliminary evaluation (consists of recording a limited number of additional traits thought to be desirable by a consensus) of users of the particular crop
Characterization and preliminary evaluation will be the responsibility of the curators while further characterization and evaluation should be carried out by the plant breeders. The data from further evaluation should be fed to the curators who will maintain a data file.

The methods of plant germplasm preservation or conservation depend  upon type of germplasm i.e. seed or non-seed clonal materials. 

Storage of Seeds

According to the storage tolerance of seeds to low temperature and dry environments seeds are further divided into storable (orthodox) and non-storable (recalcitrant) types. The orthodox seeds are those seeds which are fully desiccation tolerant. These basically include small seeded grain crops and vegetables. They can be stored for a long time under low temperature and low humidity environment and are separately stored in long, medium and short term storage conditions in accordance with the purpose of storage.

Aplant collector and conservationist faces many problems. These include adverse weather, limited availability of personnel, insufficient material because of poor growing season, general scarcity of the plant in the target region, grazing of the plant by animals, damage by pests or diseases or immaturity of the material. These problems are common to both seeds and vegetative propagules. The vegetatively propagated plant material face another unique problem related to excessive weight and bulk. The cost and inconvenience of transporting large amounts of material can be a limitation on the scope of a plant exploration mission. There is additional need to maintain selected clones in a disease-free condition for distribution to national programmes.

Molecular characterization involves the use of techniques that are available to analyze variation in plants and animals at the DNA level. Differences in gene sequences can be directly observed and described with a precision previously impossible to achieve. Many of the techniques that have been developed have already been used in evolutionary and taxonomic studies. They have immense value in studies of accessions identity and for the detection of novel useful variation in the plant germplasm. Molecular markers provide genetic information of direct value in key areas of conservation both ex situ and in situ. For ex situ conservation the key issues are:

The most important root and tuber crops cultivated on a worldwide scale are potato (Solanum tuberosum), cassava (Manihot esculenta), sweet potato (Ipomoea batatas), yams (Dioscorea spp.) and taro (Colocasia esculenta), but there are many others that are of regional, national or local importance, in a total of over a dozen dicot and monocot families. Most of these originated in tropical or subtropical areas and are mainly used as sources of carbohydrates. Many minor root and tuber crops, such as turmeric (Curcuma longa) and arrowroot (Maranta arundinacea, Tacca leontopedaloides), are also used in folk medicine and as spices (Sastrapradja et al., 1981). All these crops are vegetatively propagated. There are also vegetatively (or clonally) propagated crops that are not roots or tubers, e.g. bananas and sugarcane. A selection of the more important crops is given in Table 13.1 (Huaman et al., 1995).

Active plant breeding programmes call for continuous search for new genes to be incorporated into advanced breeding lines to face the new problems and the challenges. The rapid technological developments in gene transfer technology in recent years have opened new avenues for introducing useful genes from wild and weedy relatives of crop plants. It is also a fact that due to genetic erosion, wild species, are getting lost. Therefore, systematic collection of germplasm of wild and weedy species have become more relevant today then ever before for the following reasons (Bothmer and Seberg, 1995).

The diversity collection and making it available for future use is one of the most important activities related to management to plant germplasm resources. The collection of plant germplasm from their natural habitat is broadly known as plant germplasm exploration. The main reasons for collecting germplasm of given gene pool in a given geographical location are as follows (Engels et al., 1995)

Marshall and Brown (1975) have considered the subject of optimal sampling strategies for use in the genetic conservation of crop plants. The theme of this paper has been relatively narrow. These authors have defined a strategy for populations under imminent threat of extinction applicable to particularly the traditional landraces of some major crops such as wheat, rice and barley on account of danger of replacement of the landraces on a very broad scale due to the spread of modern cultivars. According to Brown and Marshall (1995) the emphasis in collecting of crop genetic resources has  changed radically over the years. Instead of crisis-driven broad scale crop specific programmes, the collection of plant genetic resources now involves 3 important elements as given below :

India is located between 8^°- 38^° N and 68^°- 97^ºE and exhibits extreme variation in altitude — from sea level to heights above vegetational limits in the Himalayas (ca 4500 m), and climate — from monsoon- tropical in south to temperate and alpine in the north-western Himalayas and extremely arid to semi-arid in the north-western plains. It is floristically extremely rich with about 33 percent of its botanical wealth (over 15,000 species of higher plants) being endemic. There are about 141 endemic genera distributed over 47 families. Further, of the 4,900 endemic species, larger percentage is localised in the Himalayas (about 2,532 species) than in other regions, namely, the peninsular tract (1,788 species), and the Andaman and Nicobar Islands (185 species). It is also estimated that floristic richness is maximum in the north-eastern region, which holds about 50 percent of India’s total species diversity, i.e., more than 7,000 species, and is considered as the cradle of flowering plants. Of 990 species of orchids, 700 species occur in this region (Nayar, 1989). This, thus, makes the Indian region botanically unique and interesting (Arora, 1991).

Biodiversity is the foundation stone of human survival on the earth. Very appropriately, the theme of the World Food Day celebration, 16 October, 2004 was “Biodiversity for Food Security” Biological diversity is fundamental to agriculture and food production. Scientists have identified so far about 1.4 million of the plant and animal species that exist on earth. A rich variety of cultivated plants and animals serves as the foundation for agricultural biodiversity. Yet people depend on just 14 mammal and bird species for 90 % of their food supply from animals. Just four plant species, namely, wheat, maize, rice and potato provide half of our energy from plants (FAO, 2004).

The place of origin of the crop plant can be found out on the basis of genetic affinities. This would mean to find out the geographical distribution of the wild progenitors of the cultivated plants. If one can locate the distribution of a given progenitor, one can delimit the area in which domestication could have occurred. Vavilov based his judgment on variation patterns in cultivated plants. Information on wild plants in those days was much more fragmentary than what it is today. Even today, genetic and ecological information on wild relatives of several cultivated plants is incomplete and in such cases assessment of a place of origin is difficult to make. However, in most cases required information is available on genetic affinities between wild and tame, on distribution areas and on ecological affinities of the wild progenitors. On this basis it is possible to determine the initial place of origin.

IPGRI founded in 1974 is the world’s largest international institute dedicated solely to the conservation and use of plant genetic resources. It is located at Rome in Italy. The institute is funded by the Consultative Group on International Agricultural Research (CGIR) which is an informal association of 45 public and private sector donors. CGIAR supports a network of 16 International Agricultural Research Centres. The Group was established in 1971. It is cosponsored by the Food and Agriculture Organization (FAO), the World Bank, United Nations Development Programme (UNDP), and the United Nations Environment Programme (UNEP).

Historically, Indian scientists have recognized the importance of conserving plant biodiversity with particular reference to indigenous     and naturalized crops and their wild relatives. Indian scientists have recognized the importance of conserving plant biodiversity as early as 1935 by the ‘Crops and Soil Wing’ of the then Board of Agriculture and Animal Husbandry. Late Dr. B. P. Pal, in his classic paper ‘Search for New genes’ published in 1937 emphasized the importance of germplasm augmentation and utilization in crop improvement programmes. The need was reiterated in a meeting of the Indian Society of Genetics and Plant Breeding in 1941, which inter alia discussed the subject of economic crops. Dr. B. P. Pal, working at the then Imperial (now Indian) Agricultural Research Institute approached the then Imperial (now Indian) Council of Agricultural Research (ICAR) to set-up a unit for assembly of global germplasm under phytosanitary conditions in India. The ICAR scheme for plant introduction started functioning in 1946 under the leadership of late Dr. Harbhajan Singh as the first ‘Operational Scientist’. The scheme was further expanded and strengthened as ‘Plant Introduction and Exploration Organization’ in the Botany Division in 1956 and later established as a separate Division of Plant Introduction in IARI in 1961 headed by Dr. Harbhajan Singh.

Plants are vital part of the world’s biological diversity and essential resource for human well being. Besides meeting our basic food and fibre requirement, many thousands of wild plants have immense economical and cultural importance and potential for providing food, medicine, fuel, clothing and shelter for vast number of people at the global  level. Traditional Chinese medicines use around 5000 plant species and about 7000 different plants are used in traditional medicines in India. Plants play an important role in maintaining basic ecosystem functions essential for survival of animal life. Thus, it is of vital importance to collect and conserve the plant genetic resources on a global basis. It is all the more important as several cases of genetic vulnerability of plants that are grown for food and other uses have been recorded (National Research Council, 1972, Granett et al., 1991). Genetic diversity in the germplasm of crop plants is an effective defense against genetic vulnerability. The genetic diversity is strategically used as modern plant breeding intervention to help increase stability and sustainability of food production using modern cultivars.

The National Bureau of Plant Genetic Resources, commonly known as NBPGR was established by the Indian Council of Agricultural Research (ICAR) in 1976 with its main campus at New Delhi. Being the nodal organization in India it has been given the national mandate to plan, conduct, promote and coordinate all activities concerning plant exploration and collection and also for safe conservation and distribution of both indigenous and introduced genetic variability in crop plants and their wild relatives. The Bureau is also vested with the authority to issue Import Permit and Phytosanitary Certificate and conduct quarantine checks on all seed materials and plant propagules (including transgenic material) introduced from abroad or exported for research purposes.

Allard (1960) defines germplasm as “the sum total of the hereditary  materials in  species” The word germplasm in context of plants is now referred to as plant genetic resources i.e. PGR.PGR is a component of the world’s biological diversity. The term biological diversity is a vast and often undervalued resource. Biological diversity includes every form of life, from the smallest microbe to the largest beast. Biodiversity is the variety and variability of all plants, animals and microorganisms and the ecological complexes of which they are part. The earth’s biodiversity – its ecosystems, species and genes – are the product of over 3000 million years of evolution. Throughout this time, small changes have accumulated in populations, resulting in a multitude of living forms closely adapted to the physical conditions they face and to each other. They supply all our food, much of our raw materials and energy and many of our medicines. Intact eco-systems also play a central role in the functioning of biosphere. Plants are of crucial importance in stabilizing climate, protecting watersheds on soils and maintaining the chemical balance of the earth. When key species are lost, vital ecological services are disrupted (Zedan, 1995).

Introduction

Famous French postmodern philosopher Jean Baudrillard commented on information once. “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise.” This seemingly paradox observation fits contemporary Internet age perfectly. Internet is a vast repository of information of all kinds and is bursting on its seams. The seeker needs to search with monumental grit and determination to have a full handle on the range of information available for any particular search. A search string for GIS returns more than 1185000 results, in a mere 0.26 seconds. Recommending a comprehensive resource list is not possible as new material gets continually added by way of additions and updates. However for a beginner the list given below will be handy. The flip side is GIS has innumerable resources and a broad range of content. Software vendors, organizations, individuals, universities and companies have provided a wealth of information. The readers are exhorted to make use of the materials to improve their understanding of the subject.

Introduction

The term geographic information systems (GIS) has come to mean, variously, an industry, a product, a technology, and a science. As such, the term invokes different perceptions dependent on whether the viewpoint is that of the software developer, the system marketer, the data provider, the application specialist, or the academic researcher, among others. A newcomer to the field is likely to be bewildered by the multiple uses of the same term, or, seen alternatively, different definitions of the same term. For instance some find it distasteful to use of the conventional acronym GIS and prefer Geospatial Technology. With all the variety of perceptions predicting the future of GIS is a cumbersome business. The most certain inference we can gain from the available trends is, it is growing and is poised for further growth. Geotechnology along with Nanotechnology and Biotechnology is considered as of the three most promising growth industries for the near future.

Topology building

Boolean operations

Query and model building

Customization

Introduction

At human scales, the world consists of objects, events, processes, and a background environment. Cognition is about knowledge: its acquisition, storage and retrieval, manipulation, and use by humans and other intelligent creatures. Cognition includes sensation and perception, thinking, imagery, reasoning and problem-solving, memory, learning, and language. Cognitive structures and process are part of the mind, which emerges from a brain and nervous system inside of a body that exists in a social and physical world. Spatial cognition deals with the cognition of spatial properties of the world, including location, size, distance, direction, shape, pattern, movement, and inter-object relations. GIS design will be largely influenced by spatial cognition.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

In a technology that consistently maintains a fantastic development curve with capabilities doubling up year-to-year, freezing the hardware specification is not easy or very meaningful. GIS (not geography) owes to its genesis and progress to the developments in information technology front. It may be asserted that any and all upgrade in the hardware industry will only add to its own ability to effectively process more and more data and handle more complex routines. The personal computers of today are seen to be adequate to run typical GIS software. A reference hardware specification is discussed here. GIS software development is an involved process but then the software developers have made good use of hardware capabilities available and have released upgrades consistently. Hardware developments are such that they defy a static spcification. However a reference specification as on 2009. An overview of various software packages used in the past as well as present is attempted. ESRI released a new version in 2002 and claimed that their ArcGIS is an “integrated collection of GIS software products for building a complete GIS that enable users to deploy GIS functionality wherever it is needed, in desktops, servers, or custom applications; over the Web; or in the field.” The details are discussed below. While a number of software overviews are available, this text is not aimed at providing a tutorial to any of the software packages nor do the comparisons made are to promote any particular software. Similarly, it is presumed that the reader is familiar with the hardware elements and functionality. Lot of information about the software packages are available on their respective web sites and the reader is exhorted to look for current, additional and in depth information required in the pages. A brief discussion on Liveware or human resource can be found at the end of the chapter.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.

In factorial experiment it may sometime happen that certain factors require bigger plots than other for convenience of operation of the experiment.  For example, in an agricultural experiment involving two factors, say, irrigation and fertilizer, it is organizationally very inconvenient to apply different levels of irrigation to small neighbouring plots.  But there is no such difficulty for the application of different levels of fertilizer. To meet such situations it is desirable to have different sizes of the experimental units in the same experiment.

In the foregoing experiments performed either in CRD or RBD or LSD. We were primarily concerned with the comparison and estimation of the effects of a single set of treatments like varieties of paddy, different methods of cultivation etc., and such experiments which deal with one factor only may be called simple experiments. In factorial experiment the effects of several factors of variation are studied and investigated simultaneously in a single experiment, such experiments are known as factorial experiments. In these experiments an attempt is made to estimate the effects of each factors and also the interaction effects i.e. the variation in the effect of one factor as a result to different levels of other factors.  These experiments provide an opportunity to study not only the individual effects of each factor but also interactions.  In many biological and clinical trials factors are likely to have interaction. Therefore, factorial experiments are more informative in such investigations.

In many situations, experiments may not known in advance which contrasts they wish to compare, or they may be interested in more than (m – 1) possible comparisons.  In many exploratory experiments, the comparisons of interest are fixed only after preliminary examinations of the data.
 
13.1 Scheffe’s Method for Comparing All Contrasts

Scheffe (1953) has proposed a method for comparing any all possible contrasts between treatment means.  In the Scheffe method, the type I error is at most a for any of the possible comparisons.

12.1 Introduction
 
The design of the experiment includes (i) planning of the experiment (ii) obtaining the relevant information from it regarding the statistical hypothesis under study and (iii) making a statistical analysis of the data. Design of experiment may be defined as the logical construction of the experiment in which the degree of the uncertainty with which the inference is drawn may be well defined. It may also be redefined as the choice of treatments, the method of assigning treatments to experimental units and arrangement of experimental units in varies patterns to suit the requirement of particular problems, are combined known as the design of experiment.

11.1 Introduction

The analysis of variance is a powerful statistical tool for tests of significance.  The test of significance based on t-distribution is an adequate procedure only for testing the significance of the difference between two sample means. In a situation when we have three or more samples to consider at a time an alternative procedure is needed for testing the hypothesis that all the samples are drawn from the same population, i.e., they have the same mean. For example five fertilizers are applied of four plots each of wheat and yield of wheat on each of the plot is given we may be interested in finding out whether the effect of these fertilizers on the yields is significantly different or in other words, whether the samples have come from the same normal population. The answer to this problem is provided by the technique of analysis of variance. Thus basic purpose of the analysis of variance is to test the homogeneity of several means.

10.1 Introduction

In previous lessons we have been dealing with problems connected with variation occurring in any one variable, say the income of a person, the height or weight of an individual, the wage of a worker in a factory, the sales of a shop, demand of a commodity etc.  The values of a single variable were obtained from every member of the population and the different characteristics were described by certain descriptive constants after grouping into frequency distribution.  In this lesson, we shall be dealing with problems wherein there are paired variables and every individual or member of the population exhibits two values, one for each of the variables under consideration.  Example of the two variables which are to be studies together are (i) the income and expenditure of a household, (ii) the price and demand or supply of a commodity, (iii) the height and weight of a student, and (iv) the grain yield and straw yield of paddy, (v) Height of father and height of son, etc.

We have seen the procedures of testing hypothesis about population parameters in chapter 11. Frequently our interest is to test whether a set of observed values are in agreement with those which would occur if some specified hypothesis were true. The X² statistic provides a measure of agreement between such observed and expected frequencies.

The X² distribution has a number of applications in testing of hypotheses. It is used to test, (1) the independence of attributes, (2) the  goodness of fit, (3) the homogeneity of variances, (4) the homogeneity of correlation coefficients, and (5) the linkage in genetic problems. For applying X² test procedure we have to use only the actual observed frequencies and not the percentages or ratios. Further, the samples should be independent and the observations within a sample should be non-overlapping. The expected frequency in each category should be more than 5. The number of observations should be large, say, more than 50.

8.1 Statistical Hypotheses

in attempting to reach decisions about the populations, it is useful to make assumptions or guesses about the populations involved. When the assumption or statement about a phenomenon occurring under certain conditions is formulated as proposition it is called a scientific hypotesis. We can construct criteria by which a scientific hypothesis may be ‘the  yield of a new paddy variety will be 3500 kg per hectare’. In statistical language it may be stated as ‘the random variable (yield of paddy) is distributed normally with mean 3500 kg/ha’. Such a statement or proposition about the probability distribution of a random variable is called the statistical hypothesis.

7.1 Meaning of Sampling
    
Before we consider sampling theory, we first define the terms population and sample.
 
The word ‘population’ or ‘universe’ in statistics is used to refer to any collection of individuals or of their attributes or of results of operations which can be numerically specified. For example the assemblage of heights of all the individuals in the population is called a population of heights.  Similarly we may consider the population of weights, wages, yields of grain; prices of wheat, mileages of automobile tyres, etc. a population containing a finite number of individuals or members is called a finite population.  For instance the population f ages of the workers in a factory constitute a finite population of ages. A population with infinite number of members is known as infinite population.  For example the population of pressures of temperatures at various points in the atmosphere is an infinite population. 

It has been a general notion that if an experiment is conducted under identical conditions, values so obtained would be similar. But the experiences of people have dispelled this belief. Observations are always taken about a factor or character under study, which can take different values, and the factor or character is termed as variable.
Hence, we have a set of outcomes (sample points) of a random experiment. A rule that assigns a real number to each outcome (sample points) is called random variable. The rule is nothing about a function of the variable, say, X, that assigns a unique value to each sample point of the sample space.

In day-to-day life, one comes across many statements which create doubt about certain happenings and one may want to determine the chance of occurrence of a particular happening, e.g., if one desires to know the chance of rain based on some metrological observations.  In the case of coin tossing, one wants to know what will be the chance of winning if a person always stakes for head in a fixed number of tossing. There can be innumerable such queries which one would like to make and meanwhile evaluate mathematically the chance of the occurrence of such possibilities.  This invoked the need of probability theory. The theory of probability came in existence when problems of games were referred to mathematicians like Karl Pearson, Laplace and others. Complicated applications involving advanced theory of probability and axionmatic approach of probability are not dealt within this book.

4.1 Introduction and Measuring

A measure of central tendency exhibits only one of the important characteristics of distribution, namely they typical representative figure to the whole set of its values. This measure alone will not adequately describe a set of observation unless all the observations are alike. Moreover one cannot understand completely about the data set with a measure of central tendency.  Because in real practical situation there may exits a number of sets of observations (or different distributions) whose measures of central tendency are same but they may differ individually from each other in a number of ways.  In particular two sets of values have the same mean but whilst one set of data is grouped close to the mean the other one is more spread out.  The following examples will illustrate this view point clearly.

3.1 Introduction

One of the most important objectives of statistical analysis is to summaries the information contained in the data which may be considered either as a population or as a sample (or sub-population). If the set of data consists of all conceivably possible (or hypothetically possible) observations of a certain phenomenon or a variable, we call it as population. If a set of data contains only a part of these observations, that is selected values of a variable, we call it as sample. Frequency distribution and graphical representation summarize to some extent.  They reduce a mass of data to a more understandable form but not to a single value. The aim of this lesson is to determine descriptive measures or statistical constants which will describe the nature of the data.