eChapters

Abstract

Crop cultivation is a complex process and involves a set of activities like land preparation, planting, pest control, irrigation, nutrient management, harvesting, marketing, etc. Entire cycle of crop production requires multitude of information by the farmers. Despite many efforts over the years to disseminate and transfer agriculture knowledge to the stakeholders, large amounts of expertise and knowledge are still out of reach to most of them. Agriculture knowledge may be contained in the corporate database, or it may reside undocumented inside the brain of the researchers or even stored in locations unknown to the majority of the people in the organization. Large sections of the farming community, particularly the rural folk, do not have access to the huge knowledge base acquired by agricultural universities, extension-centers and businesses. In this respect the main challenge is to find this knowledge and apply it to the decision making process involved in agriculture development.

Establishment of databases and data warehouse; knowledge discovery from database/data warehouse (KDD) and development of the mechanism of dissemination of knowledge on information networks as per requirements of user groups is gaining importance now a days. Since there is a large number of data collection agencies and equally diverse resources for which the information is collected, it is easy to visualize the heterogeneity of information. The problem is compounded by the fact that there are no common standards that are applied in data collection. Designing data warehouses to integrate the collected information poses a formidable challenge to any data warehouses architect. In order to use the information for planning and decision-making level, data have to be integrated and aggregated properly.

The term bioinformatics was coined by Paulien Hogeweg in 1979 for the study of informatics processes in biotic systems. It was primarily used in genetic and genomic studies, particularly in those areas of genomics involving large-scale DNA sequencing. Bioinformatics involves application of information technology to store, organise and analyse the vast amount of biological data available in the form of sequences and structures of proteins (the building blocks of organisms) and nucleic acids (the information carrier). There are umpteen ways to define bioinformatics, mainly owing to its rapid evolutions.

Abstract

The field of bioinformatics emerged as a tool to facilitate biological discoveries more than 10 years ago. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Structure prediction is fundamentally different from the inverse problem of protein design. While the genome of many organism have been sequenced over the last few year, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction program for genes have been developed hat try to address this problem, which consists of locating the genes along a genome.

Abstract

Protein-protein interactions play a major role in most cellular processes. Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. The study of protein-protein interactions is of great importance in determining the biological function and impact of proteins. The experimental techniques remain expensive, time-consuming and labor-intense. Computational methods play an important role at all the stages of determining protein-protein interactions. In this chapter, we describe the different types of protein-protein interactions, computational methods, currently available databases and tools, protein-protein docking and as well as protein-protein interactions as drug targets.

1. Introduction

Protein–protein interactions serve as the hub of the functioning of a living cell, coordinate biological processes as well as form core for interactomics system of living organisms. Various biological processes as signal transduction, cell-cell communication, ingestion or expulsion of any macromolecule, host parasite interaction, diseases as cancer and many other metabolic networks employ protein–protein interactions. During such a process two or more proteins interact transiently with each other or form complex for a long period of time. Thus attempts have been made for years to study comprehensive mechanism of such protein–protein interactions by various branches of sciences such as biochemistry, quantum chemistry, molecular dynamics, chemical biology and signal transduction. The availability of three-dimensional (3D) structure of proteins is a prerequisite for understanding functions of protein complexes at a molecular level as well as amino acid interactions within a complex.

Key words : CAVD, Drug Design, Bioinformatics, CADD, Co-factor, Vaccinology, infectious diseases, Jenner, immunity, Molecular modeling, Immunoinformatics, Antigen, Antibody, Live Vaccine, Alive Vaccine, Protein-peptide, Enzyme, Tools for Vaccine design,
 
Vaccine – Prevention is Better than Cure

A vaccine is a biological preparation that improves immunity to a particular disease. A vaccine typically contains an agent that resembles a disease-causing microorganism, and is often made from weakened or killed forms of the microbe, its toxins or one of its surface proteins. The agent stimulates the body’s immune system to recognize the agent

Introduction

Data Mining is a complex subject that describes a type of database application used to search large amounts of data for patterns using specific tools. Advanced statistical methods and tools such as classification, association rule mining and cluster analysis search large volumes of data for previously unknown relationships within the data. Data Mining can be used to decipher trends in a wide variety of fields such as computer science, the marketing and advertising of goods, services or products, artificial intelligence research, biological sciences and high-level government intelligence. Data mining is a logical process that is used to search through large amounts of information in order to find important data.

Databases

A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. Databases can be classified according to types of content: bibliographic, full-text, numeric and images and by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. The term database may be narrowed to specify particular aspects of organized collection of data and may refer to the logical database, to the physical database as data content in computer data storage or to many other database sub-definitions.

Abstract

Last few decades have witnessed an immense technological advancement in the education sector in India. With an advancement of technology, new gadgets are flooding the Indian market continuously bringing an immense opportunity for the education sector to improve upon the course delivery system. Higher education sector in India is adopting such new technologies in course delivery at a slow pace; however, there remains still a far way to traverse for the Indian education sector in order to achieve a perceptible and effective technology-infusion in the academic delivery arena. This paper is devoted to present an expository review on the usefulness of new gadgets, like smart phones, e-book reader as well as tablet PC, in both class-room education-delivery segments as well as in distance education sector in India.

Abstract

Fusion is a well known entity in various fields (e.g. medical imaging, weapon detection, flood monitoring etc.) that surmises information originating from several modalities. This chapter is an attempt to assort the various image fusion techniques available for human face recognition and to put a review of the advancements being made by the different researchers. The various types of fusion those have been used or may be used for face recognition have also been discussed here.

1. Introduction

With the fairly recent advent of inexpensive, rapid sequencing technologies that continue to improve sequencing efficiency and accuracy, many species of animals, plants, and microbes have annotated genomic information publicly available. It is important to use this genomic information for the identification and isolation of novel and superior alleles of important genes from gene pools of the organisms

Abstract

The study of computational toxicity prediction is very crucial and necessary, as it is quite time consuming and expensive to measure the toxicity via experimental techniques (Quan Liao et al., 2006). The use of computational approaches particularly to reduce the time and cost for the evaluation of the kinetic and toxic behaviour of any drug has been increased tremendously in recent time period (Richard Judson et al., 2004). Machine learning is a powerful tool to discover new complex combinatorial relationships in various in vitro and in vivo data information (Richard Judson et al., 2004) and has been successfully used in many diverse fields with numerous applications such as pharmacodynamics properties prediction (Czerminski et al., 2001; Livingstone et al., 2003), protein function prediction (Cai et al., 2003), medical decision making (Veropoulos, 2001), spam categorization (Drucker et al., 1999), detection of oil spills (Kubat et al., 1998), and speech recognition (Nuttakorn et al., 2001).

Introduction

The capabilities and effort to collect the geographic data has increased dramatically in the last decade to cope up with the increasing demand for the support of decision making and planning process in the state, national and global level.  From the Government sector, there is a need for spatial data to make quick responses to natural disasters, industrial accidents and environmental crises as actions will be decided based on geography. There is constant pressure to make wise decisions in a more time and cost effective and efficient manner. Accurate and current geospatial data are critical to these decisions.

1. Machine Learning

Learning denotes changes in a system that enable a system to do the same task more efficiently the next time (Simon, 1983)or Learning is constructing or modifying representations of what is being experienced (Ryszard Michalski et al., 1986). Machine learning is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. Discover new things or structure that is unknown to humanseg. data mining.A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

Abstract
    
The amount of data being collected across a wide variety of fields today far exceeds our ability to reduce and analyze without the use of automated analysis techniques. In addition, the whole data may not be of user interest and imperfect. Therefore, it is very challenging to organize this data in formal system, which provides outputs in more relevant, useful, and structured manner. There are many techniques available to retrieve knowledge from this voluminous data. This paper discusses different rough computing based information retrieval techniques to obtain meaningful knowledge from large amount of datasets. We also discuss a real life example to show the viability of proposed research.

Abstract

Currently, computers are changing from single, isolated devices into entry points to a worldwide network of information exchange and business transactions called the World Wide Web (WWW).Current web contains billions of documents and has many administrative problems and limitations; in addition to that the web content is still accessible only to humans. For this reason, support in data, information, and knowledge exchange has become a key issue in current computer technology. The success of the WWW has made it increasingly difficult to find, access, present, and maintain the information required by a wide variety of users. In response to this problem, many new research initiatives and commercial enterprises have been set up to enrich available information with machine processable semantics.

Introduction

The role of knowledge management as the key source for competitive advantage in our organizations has become a hotly debated topic. Indeed, it is a core competency for creating competitive advantage. In this environment that knowledge management is vital, therefore, at first we should determine knowledge management situation, and then to achieve a desired point, formulate a knowledge management strategy.

Abstract

Cloud computing is becoming a technology mature enough for its use in genome research experiments. The use of large datasets, its highly demanding algorithms and the need for sudden computational resources, make large-scale sequencing experiments an attractive test-case for cloud computing. So far I have seen cloud computing demonstrated using R. However, it remains to be seen a rigorous comparison of its performance using a BLAST search and its ability to cope with ever-increasing databases and open source frameworks such as bioperl or bioconductor.

“Knowledge Management is the process by which an organization identifies, creates, manages and delivers information to enhance workforce performance. The purpose of knowledge management is to provide information collected from employees, best practices, clients, competitors and other sources in a format that employees can use to the productivity and quality of their work”

Before attempting to address the question of knowledge management, it’s probably appropriate to develop some perspective regarding this stuff called knowledge. Consider this observation made by Neil Fleming [1996] as a basis for thought relating to the following points.

Now days, in every walk of our life, we deal with a lot of information. It can be for personal use or family matters or for official or business matters. We store, process and retrieve variety of information. Information is basically the collection of various facts, records, statistics, formula or methods, which we use for our official work, for our research or for other use. When all these (information, we keep/ accumulate, for possible future use) are put in an organized manner, we find it convenient to use and easy to remember/ apply. Today, as it is rightly said, information is power; information is our knowledge-strength. It is thus important to organize and manage this knowledge that mostly depends on (and derived from) an information pool.

Introduction

Individual bits or “bytes” of “raw” biological data (e.g. the number of individual plants of a given species at a given location) do not by themselves inform the human mind. However, drawing various data together within an appropriate context yields information that may be useful (e.g. the distribution and abundance of the plant species at various points in space and time). In turn, this information helps foster the quality of knowing (e.g. whether the plant species is increasing or decreasing in distribution and abundance over space and time). Knowledge and experience blend to become wisdom—the power of applying these attributes critically or practically to make decisions. This quotation is from the Atlantic Canada Conservation Data Centre, a non-profit organization established in 1999.Thus, we are led to think about Data, Information, Knowledge, and Wisdom as we explore the capabilities and limitations of IT systems.

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.

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.

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.

ABSTRACT

The concept of the Kissan Call Centres-KCCs (Farmers’ Call Centres-FCCs)) was a logical outcome of the Government of India’s (GOI’s) commitment to leverage the ICT for overcoming the constraints of distance and time in providing new generation extension services to the farmers of the country by facilitating their direct virtual contact with the agricultural extension and expert personnel. The study on KCC has shown that farmers in the age group of 29 to 48 years are the biggest user group of KCC helpline. In terms of academic background, farmers with a minimum exposure of six to seven years to formal schooling (86%) seek to utilize the KCC support and relatively inexperienced farmers with less than 10 years of experience are more likely (47.5%) to call the KCC. The scope to enhance awareness and access among women farmers to the KCC helpline is huge as their current levels of participation are negligible. As regards the performance of KCCs, 84% of the farmers who called the KCCs expressed overall satisfaction from the advice provided through the helpline.

ABSTRACT
  
The present study was conducted with 100 internet users in Lahowal CIC and Barbaruah CIC in Dibrugarh district of Assam to critically analyze the various aspects of internet utilization pattern. Results showed that majority (66%) of the respondents were between 18 to 26 years of age and were students (50%). Education qualification of majority of the respondents (32%) was above seventh standard and below graduation and seventy per cent were above graduation and below post graduation and also undergone training in computer.

ABSTRACT

As population explosion continues and the shortage of extension personnel becomes acute, the wisest proposition is to utilize the latest information and communication technology (ICT) to make information available to the common man. The ICT  tools with their recent advancements have become easily available in various forms such a mobile telephone, PDA, WLL phones, FM radio, community radio, television, wireless internet services, webcams, video conferencing facilities etc. Extension agencies need to harness the ICT tools usefully and improve the quality of extension services.

ABSTRACT

Information is basic element in any development activity. It is useful only if it is available, if the users have access to it, in the appropriate form and language. Information in the field of agriculture, to benefit farmers, has to be tailored to local agro-ecological and socio-economic conditions backed up by relevant input supply services and public policies. The impact of information revolution with its powerful information and communication technologies are making the whole universe into a small global village enabling easy access and faster sharing of knowledge among the world communities. Information Communication Technologies have great prospect and wide scope for disseminating information to the people in remote areas with great accuracy and with no loss or distortion of any information. ICTs can empower rural communities and give them ‘a voice’ that permits them to contribute to the development process.

ABSTRACT

Communication is now one of the central issues in developing societies. Knowledge expansion, information technology and technology dissemination are the three very important dimensions of any development system be it agriculture, industry and whatever. Revolutionary changes in communication policies, techniques and strategies are needed in order to make information is a fundamental right to the poor rather than making communication a privilege enjoyed by the rich and powerful. Transfer of agricultural technology generally aims at speedy diffusion of novel agricultural information from the place of invention to the place of adoption. In the current Information and Communication Technology (ICT) era, human dependency on computer and allied modern electronic gadgets for executing day to day activities is inevitable. All fields are being influenced by the interventions of ICT and agriculture is in no way an exception to it.

ABSTRACT

The study was undertaken to assess the effectiveness of ICT-Enabled Village Information Centres (VICs) with special reference to dairy farming in Tamil Nadu. Ex-post-facto research design was followed, wherein 180 users and 90 non-users of VICs constituting 270 dairy farmers covering three ICT-VIC initiatives viz., Tamil Nadu Veterinary and Animal Sciences University (TANUVAS), M.S.Swaminathan Research Foundation (MSSRF) and n-Logue communication in Tamil Nadu were selected using random sampling technique. Data collected through personal interviews were analysed using appropriate statistical tools.

ABSTRACT

The major part of the agriculture in India is Rainfed. The increasing pressure of population on land has resulted into over exploitation of land, water and other ecological resources, resulting in water scarcity, land degradation and rapid depletion of ground water-table. Government of India has adopted watershed management strategy to address these problems, but all existing watershed management programmes are mainly fragmented and governed by different departments. This has resulted in formation of providing better service delivery and governance. In this study an attempt is made to identify the scope of convergence between National Rural Employment Guarantee Act (NREGA) with Integrated Watershed Management Programme (IWMP) for generation of social capital and self-employment in the command area of a watershed using geospatial technology.

ABSTRACT

Probably since the beginning of agriculture, farmers have recognized the value of rows of trees/ shrubs planted in the farm (called as Agroforestry) which offers environmental protection, crop and livestock production and reduce the soil health hazards. The role of agroforestry is immense in the undulating terrains like North East India, rain fed area like Central India or the problem soils in general. Even though research and extension work is on the way to increase the trees out side the forest, the information and knowledge on best tree + crop combination is either scarce or limited to regions in the form of research articles, books or  journals. The recent scope of Information and Communication Technology (ICT) and its applicability due to its easy, fast and any where accessibility have opened the window of opportunity towards the scientific agroforestry and farm forestry developments. One such means of ICT is through the availability of useful databases and websites on agroforestry.

ABSTRACT

The domestic buffalo occupies an important niche in agricultural systems by providing milk, meat and draught power. Indian buffalo population which is about 1/3 of the cattle population contributes more than half of the total milk production of the country.  Several biological and socio-economic problems restricted the efficiency of buffalo production which needs careful and intensive research. Studies pertaining to reproduction are one of the important areas in buffalo research. The reproductive process in buffalo is complex in nature, which differs on many aspects with the cattle. There is a need to undertake separate strategies to understand buffalo reproduction to disseminate the information to the livestock owners and researchers.

ABSTRACT

Veterinarians, appointed in field, rarely get the opportunity to update their knowledge and skills, through trainings or courses, due to their working conditions. More over, they feel isolated and neglected from colleges due to poor interactions. Looking to this need, the courses on Post Mortem Techniques and Diagnosis of Parasitic Diseases in Livestock were developed, based on the Need Assessment survey. The courses were developed on interactive CD ROM with audio, video clips and photographs along with some reading material. The courses were of one credit each and had minimum one face-to-face session. The response by the veterinarians was tremendous and around 210 veterinarians offered for the courses by paying fees. Participants and stakeholders expressed the need for additional courses and accordingly two more courses on surgical techniques in large and small animals were launched which also received good response.

ABSTRACT

A methodology for depiction of Brown Plant Hopper (BPH) pest incidence on rabi rice crop using Remote Sensing (RS) data was attempted. The reflectance and geographically registered RS images of Feb.12, Mar.13, and April 30, 2009  as well as Feb.23 and March 14, 2007 were used to classify the Godavari district for generation of rabi rice crop area map. The rice crop map thus generated was used as an overlaying layer of rice crop mask in GIS to derive other RS based indices described below over this crop mask. Land Surface Temperature (LST) data product for rice growing areas was generated using rice crop mask. Normalised Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI) onices derived from MODIS data and NDWI from AWiFS data were correlated with pest count data for two years 2007 and 2009.

ABSTRACT

Future is the era of ICT (Information Communication Technology) which is equally applicable in the field of Agriculture. Farmers database will be an important part of ICT which is required for smooth functioning of various agricultural activities like Farmer’s Trainings, Demonstrations, Crop monitoring, Support to farmers, implementation of various government schemes etc.  This software records farmers Name, Phone number, Sex, Category (SC/ST/Gen),  Number of dependents, Annual agricultural income, whether fully or partially dependent, Land holding size,  Major crops, Village, Block, Sub-division, Land situation (Plains/ Low Hills/ Mid Hills/ High Hills), Soil Reaction, Soil type, (Highly Acidic/Moderately /acidic/Neutral/Alkaline/Highly Alkaline), NPK status, Irrigation facility and information regarding Cattle. From the above record various reports can be generated according to the needs of the scientific personnel. For example soil scientist will have record of soil pH & NPK status of different farmers across the district. Scientist can filter the database to get farmers of a particular soil type to conduct training;

ABSTRACT

Agricultural growth is a catalyst for broad based economic development in most of the low-income countries. Over the last few decades, the share of agriculture in GDP has steadily declined in India from over 50 per cent in the 1950s to 18.5 per cent in 2006-07. The role of extension personnel in this juncture has become immense and there is a mammoth task ahead in the era of contract farming, organized retailing and market led extension. Information and Communication Technology (ICT) is an umbrella that includes all technologies for the communication of information.  It caters for individualized instruction and the contents could be easily updated and expanded.