eBook Chapters

Abstract

A large tsunami triggered due to a massive earthquake of 9.3M in the offshore of Sumatra on 26^th December 2004 created havoc in several countries of the Indian Ocean. The deadly tsunami waves lashed the towns adjoining the coastline of several countries including Indonesia, Thailand, Malaysia, India and Sri Lanka causing more than 1,50,000 deaths. The earthquake occurred along the plate boundary is marked by a subduction zone between the Indian plate and the Burmese micro plate. In Andhra Pradesh, six coastal Districts were affected and amongst them four south coastal districts experienced maximum damage. In the present study, Tuppalapalem (vagarru revenue village) of Vakadu mandal, Nellore District was selected as a case study for discussion in the article. The objective of the study was to make an inventory of the damages to the natural and physical resources, by preparing pre and post-tsunami maps on geomorphology, land use / land cover, tsunami inundation and finally the tsunami risk zonation map on 1:8,000 scale. In this process, water and soil samples of the affected regions were also collected and chemical analysis was carried out. The data on damages were collected using a specially formulated data sheet. High resolution IKONOS data of January 2000 and IRS data of January 2005 were used for the generation of respectively the pre and post-tsunami geomorphology and land use / land cover maps. Ground truth data were also incorporated in these thematic maps.

The study has revealed that the water in all the bore wells became brackish after tsunami. About 80 country boats, 40 fibre boats and nets were lost with an estimated loss of about one crore. The tidal waters damaged the bunds of the aqua-farms and deposited the transported sediments in them. The extensive casuarina plantations, were reduced by the onslaught of tsunami and the casuarina trees became brown due to their contact with tidal waters. From amongst the various geomorphic units mapped in the study area, the beach, spits, shoals, creeks, swales, palaeo creeks were inundated by tsunami where as beach ridges, dune complexes and stabilized sand dunes were not affected and hence inferred as barriers. Geomorphology, land use / land cover and the tsunami inundation maps, tsunami risk zonation map were prepared showing very high, high and moderate zones of tsunami risks. It is recommended to take up casuarina plantations extensively along the coast as a biowall, which will also provide employment to the local people. The shallow aquifers of the coastal environment were contaminated by the tidal waves. Hence, drinking water should be supplied through protected water supply schemes. As a part of the community development, proper awareness must be created through training and appraisal to the fisherman community to face such natural disasters. The coastal regulation zone act, which prohibits any construction activity within 500m of the high tide line, should be strictly enforced. Keeping in view of the experience, a holistic tsunami protection plan is needed to safe guard the Indian coasts.

Abstract

The tsunami has struck many coastal countries of South Asia on 26 December 2004 and caused greater devastation along Andaman - Nicobar and Indian coasts. In India, Tamil Nadu coast is worst affected in Kanyakumari, Nagapattinam and Cuddalore districts. Subsequently, damage assessment, in respect of natural and physical resources, was done using Remote Sensing and GIS in parts of Cuddalore and Pondicherry coasts. The structural damages were studied village wise and categorized as maximum, moderate and least affected areas. As far as the natural resources are concerned, the changes in geomorphology, land use/ land cover, soil and water have been studied. On the basis of the same, the risk maps were also generated in GIS environment.

Keywords: Tsunami, Damage Assessment, Remote Sensing, GIS, Cuddalore, Pondicherry

Introduction

In agricultural and horticultural ecosystems greater extent of losses caused by pests includes insect-pests, plant diseases, and weeds and have directly affected the production. Out of these, a group of animals other than the insects also causes the considerable yield losses to agricultural and horticultural crops and commonly known as vertebrate pests such as birds, rodents and mammals. In recent times mammals such as blue bull (Nilgai), wild boars, rodents, porcupine and monkeys has gaining the status of pests and in certain cases a huge damage is being encountered (Rao, et al?, 2015). Among mammals, blue bull and wild boar both has become a serious problem in most of the Indian states for farmers in major agricultural and horticultural crops and resulting into immeasurable damage.

Soil-borne plant pathogens cause significant losses in the nursery plantation of agricultural, horticultural and forestry plantation stands. These pathogens can survive in the soil for many years through their resistant structures. Besides, the diseases caused by them are difficult to predict. Soil factors like soil type, texture, pH, moisture, temperature, level of organic matter and nutrient content influence the activity of soil-borne pathogens and diseases. In order to survive successfully, these pathogens compete with others for nutrition, space and other requirements. Understanding the dynamics of these pathogens and other microbial populations in the soil in relation to environmental conditions is essential for the successful management of these diseases.

The term dandruff includes several different conditions of the scalp. There is scaling in which the entire scalp may be covered with loose dry scales. When the hair is combed or brushed, or when the scalp is scratched, these scales fall like snow on the shoulders. Often there is itching, as well, and the scalp may be red from scratching.

What is at first thought to be dandruff may later prove to be some other conditions, such as. seborrhoeic dermatitis, or worse still psoriasis and even neurodermatitis.

Neurodermatitis : It is a chronic type of itching and inflammation of the skin. The itching area may be in some localized spot or it may extend over large areas of the body. The condition may even develop in infants. It may disappear after the age of two and return in the early teens of later. The condition is more often seen in those who suffer from fever, hives and asthma. True cause is not known for most of the people are allergic to many things. Many of them are tense and restless and are troubled with a dry skin. They are hypersensitive and are often given to itching, especially in the bend of elbow or behind the knee. The constant scratching leaves deep scratch marks and causes thickening and furrowing of the skin.

Morphological characteristics

The plant is a monoecious undershrub that grows up to 3.5 metres tall and has serrated leaves and stiff branches that emerge from the root. Lower branches feature big, typically broad, elliptical, three- to five-lobed leaves with rounded bases, while above branches have small, lanceolate leaves. Petioles range in length from 5 to 15 cm.

Floral characteristics

The flowers are unisexual. The calyx of male flowers is globose, 2.5 mm long, four to five partite, glabrous or slightly pubescent, membranous, finely mottled with a disc of six glands, and glabrous or slightly pubescent. There are approximately 20 stamens in each flower. Sepals are ovate-lanceolate and glabrous, and the disc is roughly 2.5 mm in diameter in female flowers. Fruit is a three-lobed capsule that is around 8–13 mm long and hairy. Seeds contain an oily endosperm and are speckled and smooth. Flowers bloom in January–February, followed by fruit that mature a month later.

Distribution

The species grows in tropical and subtropical places with rainfall of around 1000 mm, such as the Himalayan foothills, Kashmir to the Khasi hills, and especially in the Vindhyas southward. In North and East Bengal, Chhota Nagpur, and peninsular India, it is fairly frequent.

In 2009, the world celebrated the 200th anniversary of the life and work of Charles Darwin, a transformational scientist who brought about a revolution in our understanding of evolution.  Evolution through natural selection among living organisms leading to the survival of the fittest was a concept which was alien to Christian thought in Darwin’s time, as faith was strong in the divine creation of the living forms of our planet.  Evolution of higher forms of life, including human beings, from fish and other forms of living organisms was an accepted view in ancient Indian thought, as exemplified by the ten forms of manifestation on earth of the Hindu god Vishnu.

“Variety is the spice of life” is a common saying.  Variation is a must for selection to occur.  Today, we are confronted with the prospect of human-induced changes in climate, leading to adverse variations in temperature, precipitation, flood and sea level.  We will have to be prepared for facing the consequences of drought, flood and coastal storms more frequently.

Introduction

The most significant part of the research report is analysis and data interpretation. This chapter presents the analysis of data gathered through the questionnaire designed for the study. The study's primary data was collected from 266 (two hundred and sixty-six) Library and Information Science (LIS) professionals working in the eight-state universities in Kerala. It includes a detailed analysis of major results and pertaining information for arriving at a valid conclusion. It helps to interpret the results of the study from the proper perspective. Based on the set forth objectives and hypotheses formulated, the data collected through questionnaires were tabulated, and statistical analyses have been done to formulate the result of the study.

This chapter presents the analysis and interpretation of data gathered through a structured questionnaire to conduct the current study. Data analysis and interpretation of data is the most significant part of the research report. It contains a detailed analysis of major results about the information for arriving at a valid conclusion. It helps to interpret the results of the study in a proper perspective. Based on the objectives and hypotheses formulated, the data was collected through a questionnaire. Likert’s scale was used to assess the opinions of the respondents. 

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

5.1 Introduction

This chapter presents analysis of data collected through the questionnaire survey from agriculture libraries in Maharashtra (Constituent including University Libraries i.e. Government and Affiliated i.e. Private colleges). The purpose of the survey was to explore the status of CRS (Cyber Reference Service), various aspects pertaining to the CRS, and the ICT infrastructure used for the CRS in the agriculture universities and college libraries. It also aimed to obtain agriculture library heads’ perceptions about the CRS, and explore issues and problems affecting implementation and management of the CRS in the agriculture libraries in the Maharashtra state.

Introduction

The term analytics and data analytics are used inter-changeably. When referring to analytics, it means “analysis of data” or “what can be learnt from an analysis of data”. In an industry like fishing industry, the researchers have been slow to adopt the tools used to portray accurate representations of data. For example, a dashboard with historical data from the past ten or fifteen years displayed on a digital map can enable the researchers to decide the places where fishing can be good. This map can help in locating the points fished during specific seasons. This will enable the researchers to filter the data on various parameters like area, year, season etc. or on any other specific parameter which can give valuable information. Similarly, this type of dashboard can affect the decision making process while fishing, like one can find the monetary value associated with each trip during a specific season or compare the values associated with two different trips to gauge the cost-benefits.

Data Analytics helps in taking most informed decisions in the fishing industry if one of the goals is to increase income while ensuring sustainability. It helps in deciding whether to reduce overhead costs or to increase expenditure to avoid overhead costs in future.

Fisheries sector undergoes continuous changes with introduction of newer technologies evolved through research and development institutions. Statistics per se deals with generation of data, data management, data analysis and information generation from data. There are varied disciplines of fisheries viz., Aquaculture, Fisheries Resource Management, Fish Genetics, Fish Biotechnology, Aquatic Health, Nutrition, Environment, Fish Physiology, and Post-harvest Technology, wherein research on practical aspects is undertaken which affect production and sustainability.

2.1. Open source satellite data

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

Data refers to any collection of facts, observations, measurements, or values that are typically numerical, but can also be qualitative. It's raw material that can be processed and organized to provide context, relevance, and meaning. In its raw form, data may not carry any specific meaning and might appear as a series of numbers, text, symbols, or images.

Types of Data

• Quantitative Data: Numerical data that can be measured and expressed with numbers (e.g., height, weight, temperature).

Data Collection and Report Writing

Many a times you may be asked questions like what is the average milk production of deshi cows of your area? What is the average lactation period of CB cows of your area? or maybe what is the impact of AI programme in your area? etc., by your higher officers. How to answer all these questions? All these questions may be answered scientifically if you possess elementary knowledge on statistics. We have to frame a research design and to collect data to answer these questions.

What is Data ?

Data is a collection of facts, such as numbers, words, measurements, observations or even just descriptions of things. Example: 5, Ram, Kilogram, Good, Male, etc. Independently they do not produce any meaning.

These are special types of information, generally obtained through observation, surveys, enquiries or are generated as a result of human activities.

Once you have decided what to study and how to study, you are faced with the task of systematically collecting reliable and valid evidence upon which you can base your findings. Data (singular datum) are facts which are observed and measurable phenomena. The purpose of gathering and summarizing data is to transform them into information. The data collected in the research study may be quantitative or qualitative in nature. Quantitative data can be counted and can be computed. Such as age of a boy, marks scored by a student, distance, etc. Quantitative data are easier for analysis. We also collect qualitative data like sex of a person as Male or Female or Transgender. The qualitative characteristics can be counted, but as such cannot be computed. For example, we may get “Yes’ or “No” to a question to farmers about vaccination of his animals. For this “Yes” or “No” , we may assign 0 to “Yes” and 1 to “No” in an artificial or nominal way to categorise these qualitative data, and go for computation. So, quantitative data are expressed in the form of numerals and are amenable to statistical analysis readily. Whereas, qualitative data are descriptive in nature and can be statistically analysed only after processing and after having them classified into some appropriate categories. Data is required to make a decision in any business situation. The researcher is faced with one of the most difficult problems of obtaining suitable, accurate and adequate data. Utmost care must be exercised while collecting data because the quality of the research results depends upon the reliability of the data. There are basically two sources from which we obtain data for our research works. They are known as primary data and secondary data.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

Introduction

The biological information that are being generated by research in modern biology worldwide is so huge that it is not possible to manage through conventional file keeping or simple computing. Secondly, analysis of these data certainly needs specific interfaces, software and management facilities. Therefore, such requirements to store, analyze and manage the biological information/data in an appropriate way has led to the origin of a new branch of science called “Bioinformatics”. Here, emphasis will be given on the disciplines of research mostly from which generation of data is made. Broadly, they can be classified as follows.

Abstract

Carrying out effective and sustainable agriculture has become an important issue in recent years. A key to this is the usage of modern technologies for precision agriculture. Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As more and more agricultural data are virtually being harvested along with the crops and are being collected/ stored in databases, the same can be used in productive decision making if appropriate data mining techniques are developed/applied. As a case study for this, an experiment was conducted with four consecutive agricultural (Kharif and Rabi) seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule and multivariate regression mining techniques/algorithms were propose developed /tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather- environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system.

Keywords : Data Mining, Pest/Disease Management, Precession Protection, Wireless Sensor Networks

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

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.

ABSTRACT

The Remote Sensing data volume is vast, so high processing capacity is required. It is impossible to update spatial data without the support of high performance of RS image management, retrieval, and change detection and pattern discovery. After introducing some background information including significance of SDI updating, feasibility and advantages of satellite RS imagery used to update spatial data, the framework of updating spatial and attribute information based on RS image is proposed. In the process, the integrated processing of raster data and vector data is very important, so some functions of RS image processing and GIS software should be fused. In order to extract the anticipated images from vast image database, effective retrieval technique is vital. RS image data mining and knowledge discovery is the synergy of Spatial Data Mining (SDM) and Image Data Mining (IDM). Oriented to the demands of SDI updating to intelligent information processing, some primary issues on RSDM are analysed. It is pointed out that updating SDI by satellite RS imagery will be potential advantageous in the future. RS Data Mining and Knowledge Discovery are very important and will play important roles in the future.

Keywords: Spatial Data Infrastructure (SDI), Remote Sensing Data Mining, Spatial Data Mining, Image Data Mining.

Introduction

The value of data privacy in the linked world of today cannot be emphasised. As digital technologies proliferate and personal data is collected, stored, and analysed on a large scale, people and organisations are more susceptible to data exploitation and privacy violations. In the modern era, privacy of data are vital because of following reasons:

1. Safety Regarding Personal Information: Fundamental People’s freedom to manage their personal data protected by data privacy. In a time when everyone has a digital footprint, maintaining one’s liberty and dignity requires protecting private information such as financial records, health records, and internet activity

2. Trust Management is Critical for Organisations and Companies: Maintaining data privacy improves reputation and brand loyalty by building trust with stakeholders, clients, and consumers. On the other hand, privacy violations and data breaches can seriously harm an organization’s reputation by undermining people’s confidence in its ability to protect sensitive data.

3. Compliance with Laws and Regulations: Strict data privacy laws have been passed by governments all over the world to uphold people’s rights and make businesses responsible for handling personal information. Adherence to regulatory mandates similar to the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US not only necessary from a legal standpoint but also morally required for moral business practices.

17.1 Introduction

In an era where digital technologies permeate every facet of our lives, the discourse surrounding data privacy and security has never been more pertinent. However, within this discourse lies a critical blind spot the intersectionality of gender and technology. As we navigate the complexities of safeguarding personal data and ensuring online security, it is important to recognise and address the differential impacts experienced by individuals based on their gender identities. The combination of gender dynamics with data privacy and security unveils a multifaceted landscape characterised by unique challenges and opportunities. This intersectionality necessitates an examination that goes beyond conventional approaches, requiring a critical lens that acknowledges the diverse experiences and power dynamics at play.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

Introduction

Hydrological data visualization plays a crucial role in analyzing and understanding the behavior and characteristics of water resources. By visualizing hydrological data, we can gain insight into the distribution, variability, and trends of different hydrological variables such as river flow, precipitation, groundwater levels, and water quality parameters. This information can help decision-making in areas such as water resources management, flood forecasting, and climate adaptation.

R Language is a popular and versatile tool for hydrological data visualization, offering several powerful packages for data analysis and visualization. Each of these packages has its own strengths and limitations, which can be leveraged depending on the specific needs of a given analysis. For example, ggplot2 is an extensively used package for creating publication-quality static plots that are customizable and visually appealing. This package is based on the grammar of graphics, which provides a new plotting system that combines the benefits of both base and lattice graphics. ggplot2 can be used to create a wide range of plot types, including time-series plots, spatial maps, and scatterplots. It provides several options for customizing plot elements such as axis labels, legends, and plot themes. On the other hand plotly offers interactive and dynamic plots that can be easily shared and embedded in web applications. With plotly, we can create plots that allow for zooming, panning, and hovering over data points to display additional information. This package is mainly useful for exploring relationships between hydrological variables. In addition to these packages, hydroTSM provides functions for reading, manipulating, and plotting time-series data. hydroTSM can help us create time-series plots of variables such as river f low, precipitation, and temperature, with options for adding trend lines error bars, and annotations.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

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.

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

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.

4.1 INTRODUCTION

Database management system is a computer program to store, search, manipulate retrieve and give the result. Vast amount of spatial data are available and it is necessary to arrange them, store them for retrieval and analysis. Attribute data adds intelligent to GIS. Without database, a GIS will be just a Cad software. Cad softwares do not have database. GIS softwares have their own databases besides it has the potential to link with external database like Oracle, MS Access, MS Excel, Informix, INGRESS, Sybase and dbase. The data available in paper has to be digitally stored in a proper order. Previously the data were stored in a a flat file format where the data was stored without any structure. But to search and retrieve and analyse the database, database structures were developed. There are of 4 types of database structures namely Hierarchical, Network, Relational database and Object orientation.

Tables are made of rows and columns. Rows represent a map feature. Columns represents characteristic of map feature. Row is called a record or a tupule column is a field or an item. Table 4.1 is a table where rows 1, 2 and 3 represent the locations with the soil types. A column soil type represents various soil types.

Evolution of Computer technology and advent of database concept have provided enormous opportunities to the  scientist, researchers and managers of diverse field  to create, manipulate integrate, link and analyse their data in order to generate information and knowledge. The research projects involving exploration, survey and collection of data, generally create database to maintain and analyze the data.  Almost in every sphere of life and industry this database concept/technology is used in one form or other.  Scientists and researchers of life sciences realized the importance of the database and used this technology to develop databases of different types/groups of living organisms. The database technology has eased the difficulty in designing, developing and maintaining the complex information systems.

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

Biological databases that are composed of a large collection of digital nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer, which is made available to the research community worldwide using web servers. The vast amount of information generated in molecular biology is analyzed, curated, tabulated, and arranged in databases. There are different types of biological databases, based on their contents. Besides, the database management system, a program to manage the databases, can be of two types: distributed DBMS and relational DBMS. This chapter briefly narrates the importance and purpose of biological databases.

Date palm (Phoenix dactylifera L.) known as Khajoor, Kharek is an important fruit tree of semi-arid and arid regions of the world. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system.  It is also one of the most suitable fruit plant, which can be easily grown under saline irrigation conditions. It is well known fact for date palm that its feet in the water and its head in sun. It requires dry hot climate for growth and development of fruits. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, income generation and foreign exchange earnings. It can be grown at such areas where adequate irrigation facilities are available besides other climatic requirements (Chandra et al., 1992; Sharma and Singh, 2013).

Introduction

Date palm (Phoenix dactylifera L.; Family- Arecaceae) also known as Khajoor, Kharek is an ancient important fruit tree of semi-arid and arid regions of the world. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system. It is also suitable fruit trees for growing under saline irrigation conditions. It is well known fact for date palm that its feet in the water and its head in sun. It requires dry hot climate for growth and development of fruits. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, higher income generation and foreign exchange earnings. It can be grown at such places where adequate irrigation facility is available besides other dry hot climate conditions (Chandra et al, 1992; Singh and Dhandar, 2007). Better returns from date plantation can be achieved through good management practices besides adequate marketing of produce. The quality produce and its marketing will help to increase the income from date palm cultivation.

Introduction

Date palm (Phoenix dactylifera L.) is a monocotyledonous and dieocious species belonging to the family Palmae. The Latin name of the date palm, Phoenix dactylifera, is derived from the Phoenician name “Phoenix,” which means date palm, and dactylifera is derived from the Greek word “daktulos,” which means a finger. In India, generally, two species of Phoenix are found, of which, Phoenix sylvestris Roxb. called “desi khajoor,” is found throughout the country but t produces inferior quality fruits. The fruit contains large stones with less pulp content.

Date palm is considered one of the world’s oldest cultivated fruit trees. Archeological evidence suggests that the date palm was domesticated some 6000 years ago in the Mesopotamian region. It is widely cultivated in arid regions of the Middle East and North Africa. It is also one of the most appropriate fruit trees, and it can be easily grown under saline soil and water conditions. It grows well in poor, arid soils due to its hardy plant characteristics and deep root system. Date palm is believed to be indigenous to countries around the Persian Gulf. In the Indus Valley, date palm is believed to have been introduced by the soldiers of Alexander the Great in the fourth century B.C. and during the Muslim invasions at the beginning of the eight century AD. Date palm was commercially cultivated in Iraq, Saudi Arabia, Algeria, Iran, Egypt, Morocco, Tunisia, Pakistan, and Sudan, among many other Near East and North African countries. In India, dates are grown in the states of Gujarat, Rajasthan, Punjab, Haryana, and Tamil Nadu (Table 1).

Phoenix dactylifera commonly known as the true date palm, is a palm in the genus Phoenix, cultivated for its edible sweet fruits. The fruit’s English name as well as the Latin species name dactylifera, both come from the Greek word for “finger,” Daktulos, because of the fruit’s elongated shape. The date palm is believed to have originated in the lands around the Persian Gulf. Date palms are said to thrive with their “feet in water and heads in the sun” because they need plenty of ground water but high heat and arid weather to produce fruit. Therefore, date palms grow best in hot and arid climates.

Genus : Phoenix 
Species : dactylifera L.
Family : Palmae (Sub family-Phoenicoideae)
Chromosome No. : 2n=36

Date (Phoenix dactylifera) Finger bearing is derived from Greek word Phoenix. This ancient plant has been named phoenix after the mythological bird which was native of Egypt and also very likely after ‘Phoenicia’, an old country on the coastal regions of Syria where palms were found growing in plenty. The date palms are usually planted as ornamental for their luxurious crown which is spreading at the top. The trunk and crown is covered with stiff pinnate leaves, suckers or offshoots at the base, markedly transforms the site or scene and provide an exotic charm.

Date palm (Phoenix dactylifera L.) is an important fruit tree of semi-arid and arid regions. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system. It is also one of the most suitable fruit trees, which can be easily grown under saline conditions. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, income generation and foreign exchange earnings. It can be grown at such places where adequate irrigation facilities are available besides other climatic requirements.

The latin name of the date palm, Phoenix dactylifera is derived from a Phoenician name “Phoenix”, which means date palm and dactylifera derived from a Greek word “daktulos” means a finger. In India, generally two species of Phoenix are found. Out of which, Phoenix sylvestris Roxb. called “desi khajoor” is found throughout the country but it produces inferior quality fruits. The fruit contains large stone with less pulp content. From its crown parts, a liquid is extracted known as neera or taddi, which is used as fresh drinks and also for preparation of jaggary. The tree of wild date/ desi khajoor (Phoenix sylvestris) is found in Bundelkhand region of the country and it is suitable tree component for agro forestry system (Tewari et al., 2001). Phoenix dactylifera is commercially grown for edible fruits and value added products.

• The date is a good source of crystalline sugar.
• It is a dioecious and monocotyledonous fruit crop.
• Dates can be grown in sandy loamy soils.
• Date palm needs 3300 units of heat. (A unit of heat means a degree above a daily mean of 18°C between flowering, fruit development and ripening period.

Cultivars

• Halawi of makes tolerant of rain. It is 28.5 to 42.2 per cent Dakastage Dang Zoo, Hayni Khadrawy, Shamran (Sayen) are tolerant to high humidity. Medjoal, Bastree, Zahidi – is rain and high humidity tolerant.

Date-palm (Phoenix dactylifera L.). belongs to family Palmae and is the most common tree of desert and dry areas. Date palm is one of the oldest trees known to man kind. Date-palm is believed to have originated in the countries around Persian Gulf. It is extensively grown in Iraq, Iran, UAE, Saudi Arabia, Algeria, Egypt, Libya, Yemen, Mauritiana, Morrocco, Tunisia, Baharin, Sudan, USA, Spain, Australia, Pakistan, South Africa, Somalia, Oman and some parts of the South America. Date is a staple food of the people of the Western Iraq and North Africa. In India, it has been believed to be introduced by soldiers of Alexander the Great in 4^th century B.C. In India it is commercially cultivated in Rajasthan, Punjab, Hariyana and Gujarat.

Cultivars: Deglet Noor, Halawy, Khadhrawi, Hayani, Majhul, Zahidi, Khalaeh.

Uses

Date palms produce many products that are useful to human beings. The primary product is the date fruit, which can be eaten fresh, dried, or in various processed forms. In North Africa and the Middle East, some dates are harvested and consumed during the Khalal stage, when the fruit are still very astringent with a high tannin content (Dowson and Aten, 1962 ; Glasner et al., 2002; Kader,1992). Dates can be used in cereal, pudding, bread, pressed cakes, cookies, candy bars, ice cream, and date shakes (a California specialty). Date fruit also can be made into juice, vinegar, wine, beer, sugar, syrup, honey, chutney, pickle, paste, dip, and food flavoring (Barreveld, 1993; Glasner et al., 2002).

It is said that there are as many uses of dates as there are days in a year. The trunk and wood of date palms can be used as timber, wood, or fuel. Fiber from the trunk and leaves can be made into bags, baskets, camel saddles, cords, crates, fans, food covers, furniture, mats, paper, ropes, trays, and twine. Dried bundles of leaves (‘‘Barusti’’) can be made into shades, roofs, separating walls, and enclosures. Ribs of the leaves can be used to build boats (small fishing boats called ‘‘Shasha’’) or fishing traps. The base of the leaves and fruit stalks can be used for fuel. The pith of the palms can be made into date palm flour. Terminal buds (palm heart) can be eaten as a salad or as a cooked vegetable. Date seeds can be used as feed for livestock or strung as beads for decoration. Oil from date seeds can be manufactured into soap (Chao and Krueger, 2007). Dates are considered a balanced and healthy diet and have several medicinal uses. Leaves are source of fibre used for making ropes baskets and cordage. Walking sticks are made from leafstalk.

Semilooper: Thysanoplusia(Plusia) orichalcea : Noctuidae

Bluish green or grey coloured eggs are laid singly on lower surface of leaves.The larvae are semi loopers and feed voraciously on leaves and cause defoliation of the plants.

6.1. Origin and distribution

• Grows wild in temperate Himalayas.

• Common in Kashmir Valley, Shimla and Nainital Hills.

• Commercially cultivated in Karnataka, Maharashtra, Kerala, Tamil Nadu and Andhra Pradesh

6.2. Economic importance

• Sweet worm wood or annual worm wood

• Chemical constituents - Artimisinin

• Commonly used in garlands, bouquets and religious offerings in most part of the country.

• Leaves and flowers contain essential oil valued for its exquisite and delicate aroma and is used in high grade perfumes and cosmetics.

Deadweight cost/loss : The extent to which the value and impact of a tax, tax relief or subsidy is reduced because of its side-effects. For instance, increasing the amount of tax levied on workers’ pay will lead some workers to stop working or work less, so reducing the amount of extra tax to be collected. However, creating a tax relief or subsidy to encourage people to buy life insurance would have a deadweight cost because people who would have bought insurance anyway would benefit.

Death-to-case ratio : The number of deaths attributed to a particular disease during a specified time period divided by the number of new cases of that disease identified during the same time period.

Debt forgiveness : Cancelling or rescheduling a borrower’s debts to lessen the pain of the debt burden. Debt forgiveness is increasingly viewed as the best way to relieve the financial problems facing poorer countries. Some of these countries have to pay so much in interest each year to foreign lenders that they have little money left to spend on the long-term solutions to their poverty, such as educating their workers and building a modern infrastructure. In 1998 the world Bank calculated that around 40 of the world’s poorest countries had an “unsustainably high” debt burden : the present value of their total debts was more than 220% of their exports. Debt forgiveness has potential drawbacks. For instance, there is a risk of moral hazard. If countries that borrow too much are let off their financial obligations, poor countries may feel they have nothing to lose by borrowing as much as they can. This is why policymakers often argue that debt forgiveness should come with a conditionality clause, for instance, a requirement that countries have a track record of implementing economic reforms designed to prevent a repeat of the errors that first created the need for debt forgiveness. This is the approach taken by the World Bank’s HIPC (highly indebted poor country) initiative, launched in 1996 and expanded in 1999. However, by 2003, only eight of the 38 poor countries eligible under the programme had made enough progress in reform to have some debt forgiven.

Introduction

Work-dwelling or workplace well-being describes all aspects of the working lifespan of workers from the point of view of quality, safety and physical environment. It pronounces the workers’ feelings about their work, their working environment, the climate at work and work organization. According to WHO (2010), a healthy workplace environment not only merely finds the absence of illness but also a state of comprehensive physical, mental and social comfort. Work and health hence have a reciprocal relationship that concerns occupational health wellbeing and confers decent work environments i.e. (i) payment of reasonable income; (ii) safety-security in employment and working environment; (iii) guarantee of equal opportunities and treatment for all; (iv) enclosure of social protection for the workers and their families; (v) offering prospects for personal development and inspiring social integration and (vi) allowance of workers to express their apprehensions and to organize (ILO, 1999). In the entrepreneurial sphere, globalization along with fast industrial progress is supposed to be accountable for occupational health hazards and related workplace ill-being, work insecurity, the lack of protection for workers’ health and well-being in the form of work-related accidents, illness and stress, lack of workers’ compensation and paid sick leave, lack of maternity protection, excessive hours of work, etc.(https://www.ilo.org/). This was evidenced in the recent report of ILO (2023) where it was found that 2.3 million people around the world surrender to work-related accidents or diseases every year which links to over 6000 deaths every solitary day (www.ilo.org). Subsequently, there are about 340 million occupational accidents where 160 million are fatalities of work-related illnesses annually in the World.

Introduction

Decentralization means the action or fact of reducing or undoing “centralization” or “concentration.” The dictionary meaning of decentralization is “The process of transferring the functions from the Central Government to the local units.” Decentralization is devolving power of decision–making with the lower level of organization. In a decentralization, delegation and devolution of power are given to the lower level. Devolution implies a situation in which lower ranking units acquire greater autonomy in respect of certain defined functions, including decision-making authority. Devolution of functions, powers, and responsibilities are normally granted through legislation.

Decentralized Planning in India

With the introduction of the Community Development Programme (CDP) and National Extension Service (NES) in 1952, increasing agricultural production had been on India’s agenda in different plan periods. The success of CDP and NES was reviewed by the Balawant Rai Committee in 1957 and the committee realized that community development can be achieved only when the community understands its problems, owns its responsibilities, exercises the necessary powers through its elected representatives, and maintains a constant and intelligent vigilance on local administration. The recommendation of the committee led to the preparation of plans at the level of state, district, block, towns, and Panchayats in the second plan period from 1957 to 1962. The Fourth Evaluation Report (Programme Evaluation Organization, Planning Commission) in 1959 recommended that organized and institutional participation by the public in development programs is bound to give better, more consistent, and more lasting results. In 1977, Ashok Meheta Committee suggested changing the concept of Panchayati Raj to a political institution. In 1981, the eminent economist Prof. M.L. Dantewala observed that the merit of decentralized planning is derived from its ability to discover dormant resources and skills and its endeavor to activate them. However, there was slow progress in decentralized planning up to the eighth plan (1992-97).