eChapters

1.1 Definition of Statistics

Statistics has been defined differently by different ‘Statisticians’ from time to time. These definitions emphasize precisely the meaning, scope and limitations of the subject.  The reasons for such a variety definitions may be stated as follows:

    i.    The field of utility of statistics has been increasing steadily and
    ii.    The word statistics has been used to give different meanings in singular (the science of statistical methods) and plural (numerical set of data) senses.

In order to have a clear grasp of the subject, we give below some selected definitions of statistics which are grouped under two main heads: ‘Statistics as numerical data’ and ‘Statistics as Methods’.  The former one is in plural sense and the latter singular sense.

2.1 Quantitative Classification

It the data are classified on the basis of phenomenon which is capable of quantitative measurement like age, height, weight, prices, production, income expenditure, sales, profits, etc., it is termed as quantitative classification. The quantitative phenomenon under study is known as variable.  For example the daily incomes of different retail shops in a town may be classified as under.

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.

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.

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.

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.

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. 

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.

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.

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.

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.

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.

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.

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 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.

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.

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

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

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

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 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 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.

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 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.

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.

1.    Introduction    3
2.    History and Scope of Microbiology    4
    2.1.    Controversy over the origin of microorganisms, 5
    2.2.    Germ theory of fermentation, 6
    2.3.    Germ theory of disease, 6
    2.4.    Development of laboratory techniques and pure cultures, 7
    2.5.    Discovery of immunization, 7
    2.6.    Discovery of protection against infection, 8
    2.7.    Discovery of chemotherapeutic agents, 8
    2.8.    Microorganisms as geochemical agents, 9

1.    Introduction    71
2.    Microorganisms in Food Industry    72
    2.1.    Microorganisms as the agents of fermentation in  
        traditional fermented foods, 73
    2.2.    Microorganisms as the source of food, 83
    2.3.    Microorganisms as a source of food supplements, 86

3.    Microorganisms in Pharmaceutical Industry    92

    3.1.    Antimicrobial agents from microorganisms, 92
    3.2.     Microorganisms as probiotics, 97
    3.3.    Microbial enzymes in  pharmaceuticals, 98
    3.4.    Mushroom nutriceuticals, 98

1.    Introduction    129
2.    Soil Microbiology    129

    2.1.    Bacteria, 130
    2.2.    Fungi, 132
    2.3.    Algae and Cyanobacteria, 132
    2.4.    Protozoa, 132
    2.5.    Viruses, 135

3.    Water Microbiology    133
4.    Microbial Associations    133

    4.1.    Neutral interactions, 133
    4.2.    Positive interactions, 133
    4.3.    Negative interactions, 133

1.    Introduction    174
2.    The Thermodynamic and Mechanistic basis of Cellular Metabolism    174

    2.1.      Adenosine triphosphate, 175
    2.2.    Coenzyme A, 176
    2.3.    Modes of ATP synthesis, 177

3.      Components of Electron Transport Chain    181

    3.1.    Electron carriers in ETC, 182
    3.2.    Complex I - NADH dehydrogenase (NADH coenzyme Q reductase), 185
    3.3.    Complex II - Succinate - coenzyme Q reductase, 187
    3.4.    Complex III - Coenzyme Q - cytochrome c reductase, 187
    3.5.    Complex IV - Cytochrome c oxidase, 188
    3.6.    ATP synthese, 188

1.    Introduction    232
2.    Photosynthesis and Biomass Yields    232
3.    Biomass Properties    233
    3.1.     Polymeric and chemical composition, 233
    3.2.     Fuels analysis, 235
    3.3.     Physical properties, 236

1.    Introduction    272
2.    Alcohols    273

    2.1.    Methanol, 274
    2.2.    Ethanol, 277
    2.3.        Butanol, 283

3.    Biodiesel    284
4.    P-series fuels

1.    Introduction    302
2.    Raw Materials for Alcohol Production    304

    2.1.    Sugary substrates, 306
    2.2.    Starchy residues (cereal grains), 309
    2.3.    Cellulosic materials (crop residues), 314

3.    Microorganisms for Alcohol Production    316

    3.1.    Comparision of ethanol production by bacteria and yeasts, 318

1.    Introduction    360
2.    Feedstocks for Biodiesel Production    361

    2.1.    Fats and oils, 362
    2.2.    Alcohols, 363
    2.3.    Catalysts and neutralizers, 367

1.    Introduction    392

2.    Landfill Gas    393

3.    Biogas    394

    3.1.    Benefits of biogas, 394
    3.2.    Potential of biogas, 395
    3.3.    Biogas production system, 396
        3.3.1.    Raw materials, 396
        3.3.2.    Biogas technology, 398
        3.3.3.    Process of anaerobic fermentation for methane generation, 400
        3.3.4.    Conditions for anaerobic fermentation, 404
        3.3.5.    Sludge-algae-methane system, 407
        3.3.6.    Biogas plant, 408

1.     Introduction    424

2.    Conventional Methods of Hydrogen Production    426

3.    Biological Methods of Hydrogen Production    429

    3.1.    Hydrogen production by biophotolysis of water, 430
        3.1.1. Photolysis by cyanobacteria (Blue green algae), 430
            3.1.1.1. Nitrogenase dependent hydrogen production, 434
            3.1.1.2.  Hydrogenase-dependent hydrogen production, 437
            3.1.1.3.  Factors affecting  hydrogen production by cyanobacteria, 440
            3.1.1.4.   Bioreactors for cyanobacterial hydrogen production, 443
            3.1.1.5. Future prospects for cyanobacterial hydrogen production, 446

1.    Introduction    468

2.    Biofuel Cell    470

    2.1.    Microbial-based Biofuel Cells, 471
        2.1.1.    Microbial bioreactors producing H2 for conventional fuel  cells, 475
        2.1.2.    Microbial fuel cells producing electrochemically active metabolites in  
                the anodic compartment, 476
        2.1.3.    Microbial fuel cells operating in the presence of artificial electron relays, 477
        2.1.4.    Microbial fuel cells producing electricity during wastewater treatment, 483
        2.1.5.    Microbial fuel cell producing  electricity from acetate or butyrate, 484

1.    Introduction    496

2.    Strain Development Technologies    496

    2.1.    Screening of natural variants of microorganisms, 496
    2.2.    Genetic manipulation of microorganisms, 498
        2.2.1.    Random mutagenesis and selection, 498
        2.2.2.    Conjugation, 504
        2.2.3.    Transduction, 504
        2.2.4.    Transposition, 506
        2.2.5.      Hybridization, 507
        2.2.6.    Rare Mating, 509
        2.2.7.    Cytoduction, 510
        2.2.8.    Protoplast fusion, 511
        2.2.9.    Transformation, 511
        2.2.10.    Gene cloning and transformation (Recombinant DNA technology), 513

1    Introduction to Patent Laws    540

2.    An Overview of the Patenting Process    541

    2.1.    Inventor’s disclosure, 542
    2.2.    Patentability search, 542
    2.3.    Patent application preparation, 542
    2.4.    Patent application amendment, 542
    2.5.    Patent application examination, 543
    2.6    Main parts of a patent, 543
    2.7.    Granting of patent, 544
    2.8.    Date and term of patent, 544
    2.9.    Provisional specification, 544
    2.10.    Complete specification, 545
    2.11.      Cost of patenting, 545

The Indian agriculture is characterized by agro-ecological diversities in soil, rainfall, temperature and cropping system. Besides favorable solar energy, the country receives about three trillion cubic meter of water from rainfall. Of the total geographical area of 329 million hectares the net sown area has remained at about 140-142 million hectares during the last 40 years. The gross cropped area however has increased to about 200 million hectare in 2011-2012 (Anonymous, 2013). The total operational holding is estimated at 105 million with average size of the holdings of 1.6 ha. The Indian agriculture is not only characterized by small fragmented land, but also hill farming and shifting cultivation are also practiced. Out of an estimated 142 m ha net cultivated area around 40% is irrigated and rest is rain fed. About 65% of the population depend upon agriculture for their livelihood.

Introduction

The work on field drains and farm ditches are essential for providing proper irrigation to the crop. Leveling of field is equally important for smooth operation of various field activities including irrigation. Land grading in irrigated agriculture helps in uniform application of water, better water regulation and saving in irrigation time. Land grading helps in conservation of soil and moisture under rainfed conditions. It provides desired surface drainage under irrigated and rainfed conditions. Land leveling helps in the smooth operation of sowing/planting and harvesting equipment.

Tillage operations are the basic operation, which need to be performed at proper moisture and temperature condition (climatic factor) to attain high degree of germination and better plant growth. Tillage may be defined as mechanical manipulation of soil for the purpose of germination and plant growth but it also does the followings:

The seed drills and planters developed and manufactured in advanced countries are designed to meet the requirements of farmers for the different crops and agro-climatic conditions. These machines are capable of operating at high working speed, cover the large area per unit time and provide the desired plant emergence and stand in the fields. The machines are available to handle the seeds of cereals, pulses, oil seeds and other crops widely grown in the developed countries. In developing countries even though these machines can be made available but they are beyond the reach of small farmers. The level of mechanization of sowing and planting is at a very low level.

Weeds are a serious menace and these reduce the quality and yield of crops substantially. Chemical as well as mechanical methods are being used to control the weeds. The weeding was traditionally done by “khurpa” up to mid 60s. This tool requires its operation in a sitting posture, which reduces the work rate. The use of long handled tools like “kasola” became popular because these allowed their operation in a standing posture, which increases the mobility and thus the work rate. Weeder is a mechanical device to remove the weeds (unwanted plants) from the cultivated crop. A wide variety of weeders are used in the countries, which may be categorized in three groups viz. manual weeder, animal or tractor drawn weeder and power weeder or self-propelled rotary weeder (Singh, 2007; Pandey et al., 1997; Pandey and Ganesan, 2005).

Fertilizers are required where soils are deficient in plant food elements. When land is planted to crops over a long period of time, the plant food elements slowly get reduced and productivity of crop goes down. Sandy soil looses plant food elements rapidly because heavy rainfall or application of irrigation water leaches these elements out from the soil. Adding chemical fertilizer increases fertility of soil. Principally nitrogen, phosphorous and potassium are added to the soil to promote greater yields. Uniform distribution and proper placement of fertilizer in the soil have become increasingly important factor in providing maximum crop response at minimum cost. Placing the fertilizer near the seeds at the time of sowing results in more effective utilization of nutrients. In the row crops side dressing of fertilizer is done which has immediate benefit when placed in the moist soil within the root zone.

The application of agricultural chemicals for control of insects, pests, fungi, weeds and plant diseases plays an important role in crop production. Before 1980 few chemicals were known for control of plant pests.  The chemical pesticides have played an important role in the rapid advancement of agricultural production. Chemical pesticides are getting popular due to non availability of labours especially for weeding operation during kharif season. The chemical pesticides and herbicides are applied with the help of different types of sprayers and dusters.

Harvesting and threshing are the key most important operations in the entire range of field operations required in any crop production system. Both of these operations remain still the most important operations as fruit of labour depends upon them. Crops are harvested after normal maturity with the objective to take out grain, straw, tubers etc without much loss.  It involves cutting / digging/ picking, laying, gathering, curing, transport and stacking of the crop.  Harvesting of major cereals, pulse and oilseed crops are done by using sickle whereas tuber crops are harvested by country plough or spade (Fig. 8.1).

Management of paddy straw is one of the important problems being faced by the farmers. Burning of rice straw in the field is the traditional method being practiced by most of the farmers, which causes atmospheric pollution and nutrient loss. There are three options available to manage the straw viz. i) burning the straw in the field, ii) collecting/baling the straw from the field, and incorporation of straw in the field. If rice straw is not burnt then baling may provide an attractive economic environmentally safe option. Manual collection of paddy straw is a laborious job and also storing the large volume of straw is another problem. As for as paddy straw is concerned, it has higher silica content so animals do not prefer it.

Abstract

While, life on Earth is dependent on microbes for many essential services, microbes are intimate partners in virtually every life process of plants by way of mobilization/acquisition of nutrients, out-competition and antagonism of pathogens, plant stress alleviation, plant growth promotion, co-metabolism, degradation of toxins, etc. Exploitation of such interactions between microbes and plants and optimization of plant microbiome could allow farmers to apply less chemical fertilizer, and pesticides besides improving overall plant health which paves the way for sustainable development. What is less appreciated, and less well understood, is the mechanism of microbes exerting such benefits to the plants. A greater understanding of how plants and soil microbes live together, communicate, build innate plant immunity, triggers hormone synthesis and benefit each other can therefore provide new strategies to improve plant productivity, while helping to protect the environment and maintain global biodiversity.

Introduction

Biological nitrogen fixation (BNF), generally found in legumes, is a valuable nature’s gift through which atmospheric nitrogen is fixed by symbiotically associated rhizobia that make it available to plants. Among cereals, rice (Oryza sativa) is an important staple food crop that feeds a large proportion of the world population [1]. There is tremendous pressure on agriculturists worldwide to increase the yield to meet growing consumption to feed a world population increasing day by day. However, unlike symbiotic plant–microbe interactions commonly observed in legumes, in several cereal crop plants including Oryza sativa, endophytic associations have been observed [2-8].

Abstract

The structure and function of proteins involved in plant-microbe interactions is investigated through large-scale proteomics technology in a complex biological sample. Since the whole genome sequence are now available for several plant species and microbes, proteomics study has became easier, accurate and huge amount of data can be generated during plant-microbe interactions. Proteomics approaches are highly important and relevant in many studies and showed that only genomics approaches is not sufficient as much significant information are lost as the proteins and not the genes coding them are final product which is responsible for the observed phenotype.