eChapters

1. INTRODUCTION

Agriculture plays a crucial role in addressing the needs of a growing global population, and is inextricably linked to poverty eradication, especially in developing countries. Sustainable agriculture and rural development are essential to the implementation of an integrated approach to increasing food production and enhancing food security and food safety in an environmentally sustainable way. Attaining food security has been our major concern since independence. Appropriate policy measures in agriculture made it possible to achieve food security as well as required national confidence to meet the growing food demands, despite a growing population. The increased agricultural production has been achieved largely through an increase in the yield of grain per hectare, rather than from an increased cultivated area. The increase in production has been the result of widespread cultivation of new high-yielding varieties coupled with a package of agricultural inputs that permit the genetic expression of their high yield potential. India is now even in a position to export agricultural commodities, fish products, milk products and earns a lot of foreign exchange. The present share of the agricultural sector in total exports is 15 to 17 percent. However, the export of many agricultural commodities show a high degree of fluctuations, caused by time lags between production decisions and delivery to the market. For the Government, unforeseen variations in export can complicate budgetary planning and increase risks for attainment of debt targets. For exporters, price volatility increases cash flow variability and reduces collateral value in the market. There is a need for an aggressive export growth-oriented policy to raise the share of agricultural sector in total exports to 25%. This should come through diversification of agriculture. In our country, the exports have been allowed only after domestic requirements are met, which is the major cause of fluctuations in exports from year to year. Modelling and forecasting such fluctuating data of commodities is crucial for the sake of Government as well as for the exporters.

1. INTRODUCTION

Growth is a complex biological process that must be evaluated carefully if a profitable combination of growth and efficiency is to be realized. Knowledge relating to birth weight, mature weight, maturity rate and the point of infection of the growth curve in various breeds and crosses is very useful in selecting breed combinations that can produce the most efficient growth pattern. The effect of cow size on economy of production has been a discussing subject among cattlemen and animal scientists. Growth models are used to predict rates and change in the shape of the organism. They can be applied to determine the food requirements so as to get a desired growth. The estimated parameters of growth function can evaluate various growth characteristics of animal like rate of maturing, rate of gain, mature size and related characters.

1. INTRODUCTION

The concept of product management in market research is all about tradeoffs. Whether the objective is to increase market share, profit margin or revenue, every product manager makes trade-offs—quality vs. cost, time to market vs. breadth of features, richness of the offering vs. ease of use, etc. So, how does anybody know what the market wants? What market segments exist? What those segments prefer? What will they pay? or what trade-offs to make? The answer is to get the market to make the trade-offs. The conjoint analysis is the tool to measure the trade-off i.e. to understand customer preferences, values and choices. Conjoint analysis is used for many marketing decisions, including new product development process, selecting among alternative product designs, targeting, and pricing. It is also used to predict their choices for future products and services. Conjoint Analysis assumes that a product can be “broken down” into its component attributes. For example, a car has attributes such as colour, price, size, miles-per-gallon, and model style. Using Conjoint Analysis, the value that individuals place on any product is equivalent to the sum of the utility they derive from all the attributes making up a product. Further, it assumes that the preference for a product and the likelihood to purchase it are in proportion to the utility an individual gains from the product. It is generally inappropriate for products which are evaluated by consumers on the basis of their “image”, such as beer or cigarettes, rather than on the basis of their constituent attributes. It is so widely used in market research that helps to understand the market’s preferences including product development, competitive positioning, pricing, product line analysis, segmentation and resource allocation.That gives the better insight to the market revenue, share, profit which in turn helps to provide the better market intelligence. The present chapter deals with the genesis, usefulness and analysis procedure of conjoint analysis in different software with example.

ABSTRACT

Wavelet analysis is a technique possessing mathematical properties and it has been used by the scientific research community for its power to analyse rapidly changing transient signals used to represent data or functions - the wavelets used in the analysis are, however, functions that break the data down into different scales or resolutions. Wavelets possess the competence to handle spikes and discontinuities than traditional Fourier analysis and make the noisy data to de-noisy. Though traditional applications of wavelets have focused on image compression and analysis, wave propagation but they are also being used to analyse time series, biological processes, spectroscopic data of chemical compounds, seismic signals for earthquake prediction, atmospheric data for weather prediction, computer graphics, medical image technology, detection of aircraft and submarines, non-parametric regression, density estimation, and long memory wavelet models. Indeed, wavelets permit complex information such as music, speech, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently being reconstructed with high precision. Signal transmission is based on the transmission of a series of numbers, indeed, the series representation of a function is important in all types of signal transmission.Wavelet transform of function is the improved version of Fourier transform. Fourier transform is a powerful tool for analysing the components of a stationary signal. But the Fourier transform fails to analyse the non-stationary signal where as a wavelet transform allows the components of a non-stationary signal to be analysed.

INTRODUCTION

According to analyst firm Gartner, Big Data is at the portion of the hype cycle called the "peak of inflated expectations." The business world is awash with all sorts of claims about the magic of Big Data and how it will transform industries by increasing productivity and profits and opening up opportunities that nobody even knew existed. But this will only happen if companies are able to hire enough people who actually understand what Big Data is, how to collect it, and preserve it. Computing and analytical skills are also required to get Big Data to reveal its hidden secrets and visualise it in novel ways. And there unfortunately, is the rub. There are just not enough data scientists, people with the required skills to satisfy this unmet demand. The shortfall in Big Data experts is set to rise and in the UK alone, one digital industries employer body has predicted there will be a need for 69,000 of these experts in the next five years. This claim is not original. Back in 2011, McKinsey & Co was claiming a US shortfall in Big Data experts of 140,000 - 190,000 by 2018.The shortfall in Big Data experts is being manifested in a number of ways. The first and most obvious is through recruiters casting an ever-widening net in their search for appropriate talent. There is some agreement that Big Data analysis and data visualisation requires skills in computing as well as statistics and mathematics. This has meant that university graduates with statistics, computer science and engineering have been the main source of potential employees. Lately this has widened to include subject areas such as astrophysics and computational chemistry.

ABSTRACT

The details of a frame work for development of data warehouse are discussed in this paper. The concepts of data ware housing, its data ware house process, its architecture, components, data sources, data transformation, apart from free and shareware and commercial software available are described. The details of power of Data Warehouse are also described with a procedure for a better reporting of data and analysed results. The data Warehouse should be made to deliver clear indications on how the research or business enterprise is performing. There is a need to plot out the expected users for the Data Warehouse in the enterprise so that they will have the appropriate reports in a format which is quickly understandable.

ABSTRACT

A Distributed Database is a database that is under the control of a central database management system (DBMS) in which storage devices are not all attached to a common CPU and may be dispersed over a network of computers interconnected through internet. To ensure that the Distributive databases are up to date and current, there are two processes: replication and duplication. Replication involves using specialized software that looks for changes in the distributive database. Once the changes have been identified, the replication process makes all the databases look alike. The replication process can be very complex and time consuming depending on the size and number of the distributive databases. Duplication on the other hand is not as complicated. It would basically identify one database as a master and then duplicates that database. This is to ensure that each distributed location has the same data. In the duplication process, changes to the master database only are allowed. This is to ensure that local data will not be overwritten. Both of the processes can keep the data current in all distributive locations. The most commonly used databases are DB2, MySQL, Oracle, MS-Access, Ingres, Firebird, MS SQL Server, Teradata, Sybase, Informix and Postgre SQL. Use of single or combination of databases depending on the requirement is key to success. This distributed data management with different levels of transparency like network transparency, fragmentation transparency, replication transparency, etc. Increase reliability and availability of data to the users as well as reduces the database downtime and increase the database performance, reduces network failure due to reduction of network traffic.

SUMMARY

The vast amounts of biological data generated from various studies have become the cornerstone of bioinformatics. The very first challenge of this enormous data is to store and handle this large volume of information through the establishment and use of computer databases. These data describe a unique array of information on biological entities such as DNA, protein and other molecules. However, still a large amount of data remains hidden which needs to be explored by accessing these data from databases.

Biological databases play a vital role in the biological research from the fundamental molecular biology to the applied areas of biology viz. medicine, agriculture, biochemical industries etc. This chapter introduces some basic concepts related to databases such as features, construction and classification of biological databases, submission and retrieving of data from biological databases.

INTRODUCTION

Functional genomics is the science where genes are employed to determine their function. The gene expression is an approach to analyze the functional changes in the genes. The expression level for a gene across different experimental conditions are cumulatively called the gene expression profile and the expression levels for all the genes under an experimental condition are cumulatively called the sample expression profile. Different techniques for gene expression analysis have been used so far. cDNA-AFLP (Bachem et al., 1996), SAGE (Serial Analysis of Gene Expression) (Neto, Neto & Costa, 2012) and MPSS (Massive Parallel Signature Sequencing) (Brenner et al., 2000) are the techniques for expression analysis where the genomic or transcriptome sequence of the organism is not needed. Some advanced and largely used techniques are Microarrays (Schena et al., 1995) and Real-time PCR (Higuchi et al., 1993) where the previous knowledge of the sequence is required. These two techniques are most frequently used for gene expression analysis (Perez-Torres et al., 2009). DNA microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples (Jiang, Tang & Zhang, 2004).

Energy is vital for the whole universe. Since the formation of the universe, energy acts a specific role. Steven Hawkins, George Ellis and Roger explained “The Theory of Relativity “Time and space had a finite beginning than corresponded to the origin of matter and energy from the time of BIG BANG.”. Before invention of fire, energy hovers everywhere but people did not have any knowledge about that. Living being requires energy for every day work which cells of the body provide them. Energy is also a basic requirement for the existence and development of human life. Energy plays an important role in the entire process of evolution, growth and survival of all living being and also in the social –economic development and human welfare of country. Energy is defined as the ability to work. Then we run or walk or do any type of work, we burn the energy in our body produced in cells by transforming food. Cars,planes,trolleys,ships, aeroplanes etc and all kind of machineries require energy to run. There are many sources to get energy as coal, natural oil, natural gas, wind, water, wave, sun and nuclear etc. The commercial sources of energy as fossil fuels (coal, oil, and natural gas) hydro-power and then nuclear power provide the need of energy now of the world. The demand of energy is growing an alarming rate. As for example, according to International Energy Agency the Global consumption of energy has increased from 4606 Mtoe (Million Ton Oil equivalent) in 1973 to 7287 Mtoe in 2003.

INTRODUCTION

The evolution of National Natural Resource Management System (NNRMS) towards fully harnessing the potentials of space remote sensing and the development of the series of Indian Remote Sensing Satellites, besides establishment of necessary ground based data establishment of necessary ground based data reception processing and dissemination systems as well as remote sensing facilities at National Remote Sensing Centre (NRSC), Space Applications Centre (SAC) and Regional Remote Sensing Service Centers (RRSSCs) for efficient and effective analysis of remotely sensed data are the major steps accomplished in pursuit of this goal. With the establishment of Remote Sensing Applications Centers in several States under many Governmental organizations, remote sensing today has come to stay as an integral part of the national development efforts in the vital sectors of agriculture, hydrology, geology, forestry, oceanography, mineral resources and disaster management like drought, flood, cyclone, earthquake, landslides crop pests, forest fires etc., thus touching every facet of national development. Today, India has acquired a strong self-reliant base to harness the full potential of this technology and as a result, the national objective of achieving sustainable development at micro level is being addressed through the integration of remotely sensed data with other relevant collateral information to arrive at locale specific, environment friendly, economically viable and culturally acceptable treatment packages (Rao, 1991 and Jayaraman, el at. 1993).

1. INTRODUCTION

Water is the source that upholds every part of life on earth and is a key element of sustainable development. It is essential to the survival of healthy and safe lives of mankind. Ecosystems are inevitably linked with water. Lack of access to ample and safe drinking water is a serious setback among human population. Noxious substances from industries when enter into lakes, streams, rivers, oceans, and other water bodies, they get dissolved or lie suspended in water or get deposited on the bed. This results in the pollution of water whereby the quality of the water deteriorates, affecting aquatic ecosystems.

The strain of escalating population, growth of industries, urbanization, energy intensive life style, loss of forest cover, lack of environmental awareness and implementation of environmental rules and regulations, environment improvement plans, untreated effluent discharge from industries and municipalities, use of non-biodegradable pesticides/fungicides/herbicides/ insecticides, use of chemical fertilizers instead of organic manures, etc are basis of water pollution. The pollutants from industrial discharge and sewage, besides finding their way to surface water reservoirs and rivers are also percolating into ground to pollute ground water sources, threatening food security, access to safe drinking and bathing water and providing a major health and environmental management challenge. Contaminated water from inadequate waste water management provides one of the greatest health challenges restricting development and increasing poverty through costs to health care.

Water pollution is a sensitive problem all over the world. In the wake of increasing urbanization and industrialization, the pollution potential is gaining thrust day by day. Monitoring of waste water quality parameters is currently a subject of growing concern globally. Oxygen, although poorly soluble in water, is fundamental to aquatic life. In the absence of free dissolved oxygen, aquatic systems become inhabitable to most aquatic flora and fauna. Because the solubility of oxygen in water is so low, even little decrease in dissolved oxygen levels can compromise the health of natural water systems. Organic pollutants requiring oxygen for their decomposition exert a rapid and direct influence on the ecology of these environments. Therefore it is required to monitor the load of organic pollutants in the waste water.

ABSTRACT

Supercritical fluid extraction technology specifically employing carbon dioxide as an extracting solvent under supercritical conditions have gained tremendous importance in the commercial applications as well as academic fields. The basic advantage is its operation above pressure and critical temperature. The most convenient things are variations of pressure, temperatures, batch time, flow rate and fractionation of important constituents of essential oils, herbs and petrochemicals. In the recent times most of the researchers have studied the Organic synthesis under supercritical conditions with the improved yield and reaction selectivity. The chapter also describes the thermodynamics also.

Sustainable development and environment friendly technologies in agriculture are the need of the time. It is also necessary that these technologies should be able to solve farmers’ problems that vary according to his socioeconomic and ecological condition. To reach the wide as well as diverse section of our producers, the extension system should be location specific and very fast. The technology must be based on farmer’ need, existing resources of farmers, appropriate marketing and should be considered the cost benefit ratio. Sustainable development of producers, efficient technology as well as effective transfer of technology within a short period of time is thus utmost important in recent period. These require larger proportion of human skill combined with electronic information system. In this connection, expert system can be effective in transfer of technology to meet the need of the time. Expert system is the turning point and breaking through of artificial intelligence developing, it is breaking through from generic thinking to special knowledge applying, from theory method to actual system design. Over the years the field of application of expert system has been widening rapidly. Many countries have made expert system in different field, such as: financial decision-making, medical treatment and diagnose, diagnose on breakdown of machinery, chemical engineering, geological prospecting, military affairs, and other agricultural domain. Recently, many researchers in India has been identified the advantage of this system and already developed expert system in several commodities in agricultural crops.

ABSTRACT

Expert systems are very popular computer programs designed to simulate the problem solving behavior of human thinking in a narrow domain. The world has changed into a global village. Information has become a powerful commodity and agricultural production has evolved into a complex business requiring the accumulation and integration of knowledge and information from many diverse sources. Rice is the staple food of over half the world’s population. India is the second largest producer and consumer of rice in the world. In order to remain competitive, the modern farmer often relies on agricultural specialists and advisors to provide information for decision making. In order to alleviate this problem, expert systems were identified as a powerful tool with extensive potential in agriculture. Rapid development of web technology benefits our decision-making and various activities in agriculture. With this background, web based rice expert system has been developed for diagnosing pest and disease problems of rice crop.

Key Words: Rice, Expert system, Knowledge, Decision making

INTRODUCTION

There is growing concern in both the scientific community and the general public over the possibility of important climatic changes due to increases in relatively active gases in the atmosphere, a phenomenon commonly referred to as “climate change.” In the past century, the activities of a rapidly expanding and industrializing human population have added significant quantities of heat retaining gases to the atmosphere. For example, the Intergovernmental Panel on Climate Change (IPCC) estimates that by 2100 increases in greenhouse gases will likely lead to an increase in temperature of between I0⁰ C and 3.50⁰ C (Houghton et al., 1996) and an increase in sea level of between 13 and 94 cm (Warrick et al., 1996). The IPCC also reports that warmer temperatures will most likely intensify the hydrological cycle, leading to an increase in the precipitation intensity and number of storm events (Houghton et al., 1996). Our results of this study also agree with it.

INTRODUCTION

Artificial Neural Network (ANN) is a branch of the field in computer science known as “Artificial Intelligence (AI)”. The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, machine learning, and artificial neural networks. A tremendous growth in interest of this computational mechanism has occurred since Rumelhart(1986) rediscovered a mathematically rigorous theoretical framework neural network. Traditional (physically-based) modeling of physical processes tries to explain the underlying processes. On the contrary, the data-driven models are based on a limited knowledge of the modeling process and rely on the data describing input and output characteristics. Data-driven models are based on pure relationships between input and output data and not the physical principle linking input and output. They offer real advantages over conventional methods, including the ability to handle large amounts of dynamic, non-linear or noisy data and such tools can be especially useful when the underlying relationship are not fully understood. Data-driven modeling uses results from such overlapping fields as data mining, artificial neural networks, rule-based type approaches such as expert systems, fuzzy logic concepts, and machine learning systems.

Artificial neural networks are suited to problems where relationships between the variables to be modeled are not well understood. Mathematically, an ANN may be treated as a universal approximator. The ability to learn and generalize ‘Knowledge’ from sufficient data pairs make it possible for ANN to solve large-scale complex problems such as non-linear modeling, pattern recognition, classification, control and others – all of which find application in water resources management today. Since the early nineties, ANNs have been successfully used in water management area such as rainfall-runoff modeling, stream flow prediction, groundwater modeling, water quality, hydrologic time series, and reservoir operation.

ABSTRACT

Nanomaterials will potentially play an important role in water quality. Production and applications of nanomaterials in industry are increasing. Furthermore, there are a handsome number of marketed nanomaterial-based consumer products. Therefore, how nanomaterials diagnose and function and how they perturb normal biological systems all become a top concern. In recent years, researchers are actively engaged in such investigations and discussions. At the same time, technologies and methods are developed to make more targeting nanoparticles for use in water.

Key words: Nanomaterial, Applications,Water quality

INTRODUCTION

Plant diseases caused by microbial plant pathogens and sub microscopic entities including virus and viroids induce both quantitative and qualitative losses of high magnitude in yields. The extent of loss may range from slight to 100% depending on the susceptibility/resistant level of the cultivars and prevailing favourable environmental conditions (Narayanasamy, 2011). With the introduction of high yielding cultivars, adoption of modern production techniques such as high input agronomic practices, changes in cultural practices, free movement of planting materials and changes in weather conditions over the years have led to increase in pest and disease problems, several of which have become major constraints in their successful cultivation (Gupta and Thind, 2006). The challenge is how to feed the growing population by producing more on a stagnant or shrinking landscape; with lesser input cost and with lesser hazards to the eco-system. The detection and management of diseases prior to economic loss will help to minimize the yield loss of the crops. Physical and chemical techniques have been applied either alone or in combination with certain cultural practices to enhance the effectiveness of reducing pathogen inoculums and consequent reduction in disease incidence.

1 INTRODUCTION

Aquaculture has been growing at higher rate than any other animal food-producing sector globally with Asia dominating the production. Considering the higher consumer demand for aquatic animal food, coupled with stagnating harvest from capture fisheries the rapid growth of aquaculture is expected to continue in the future. For instance, the global production of farmed food fish including finfishes, crustaceans, molluscs, amphibians (frogs), aquatic reptiles (except crocodiles) and other aquatic animals such as sea cucumbers, sea urchins, sea squirts and jellyfishes was 59.9 million metric tones (mmt), up by 7.5 percent from 55.7 mmt in 2009 (FAO, 2012). Asia accounted for 89 percent of world aquaculture production by volume in 2010 with China alone accounting for more than 60 percent of global aquaculture production volume in 2010. The other major producers in Asia are India (4.65 mmt), Viet Nam (2.67 mmt), Indonesia (2.30 mmt), Bangladesh (1.31mmt), Thailand (1.29 mmt), Myanmar (0.85mmt), the Philippines (0.74 mmt) and Japan (0.72 mmt). Further, in light of increasing population pressure on land, intensification of aquaculture practices has been taking place and now two-third of all food fish production aquaculture is achieved with artificial feeding (FAO, 2012). In recent years it also has become apparent that aquaculture production is vulnerable to adverse impacts of diseases and environmental conditions as farmed organisms are susceptible to a wide range of factors that can pose a major threat to a thriving aquaculture industry with considerable economical repercus-sions. For instance, in 2010, aquaculture in China suffered production losses of 1.7 million tonnes caused by natural disasters, diseases and pollution. Disease outbreaks virtually wiped out marine shrimp farming production in Mozambique in 2011 (FAO, 2012). In addition, impacts of culture environment, and harvest and post-harvest handlings on flesh quality are also increasingly becoming important issues.