eBook Chapters

Artificial insemination (AI) technique in pigs is being performed since several decades in many countries but not widely accepted technology as in cattle due to reasons like sub-standard method of semen cryopreservation and lower fertility rate. The main idea is to increase the breeding efficiency by utilization of superior boars. The number of boars needed for breeding purposes can be reduced by use of AI and reduced transmission of infectious diseases. A minimum of 15 to 20 sows can be inseminated after dilution over a 2 days period with an ejaculate from a boar. Artificial insemination had considerable impact worldwide in its application to commercial pig production. The main advantage of using fresh liquid boar semen is that fertility is maintained even with low number of spermatozoa in the inseminate. AI provides unique opportunity for the genetic improvement of pig industry. Using fewer than 1 million sperm cells per breeding unit can achieve conception rates with liquid semen similar to those with frozen thawed semen containing approximately 15 million sperm and reduced fertility makes cryopreservation an uneconomical option in boars.

Artificial insemination Technology is a high cost and labour intensive technique in sheep and In India, sheep are reared by economically weaker sections of the society and any advanced technique may take much time to reach their flocks.  Considerably more research work has been done in AI with sheep than with goats. However, the equipment and techniques are very similar for the two species. Goat semen may be stored and frozen more successfully than ram semen.

Advantages and limitations of artificial insemination

Artificial insemination is the first great biotechnology applied to improve reproduction and genetics of domestic animals. Artificial insemination includes the collection of semen from the male, semen assessment for quantity and quality, dilution with extender, preservation and then insemination. Artificial insemination (A.I.) is a breeding technique where semen with living sperm is deposited into the reproductive tract of a female who is in estrus by use of instruments. Artificial insemination is the most important single technique widely used worldwide to upgrade the genetic makeup of the farm animals. Artificial insemination can be performed in most of the domestic animals like cow, buffalo, mare, sow, bitch, queen cat, lambs, does etc. Due to invention of frozen semen technology, the extended semen doses can be preserved for years together and permit wide spread use of superior germplasm. Artificial insemination is a powerful tool mostly employed for livestock improvement. The advantages and limitations of frozen semen technology are enlisted below:

9.1. Introduction

The human population of the world will reach nearly 10 billion by 2050, boosting agricultural order-in a situation of humble financial development (FAO, 2017). At present, approximately 37.7% of total land surface is being utilized for crop production. From employment generation to contribution to National Income, agriculture sector is contributing a significant role in economic prosperity of the developed nations and is playing an active part in the economy of the developing countries as well. The growth of agriculture has resulted in a significant increase in the per-capita income of the rural community. Thus, a greater emphasis on agricultural sector is the need of the hour. For countries, like India, the agricultural sector accounts for 18% of GDP and provides employment to 50% of the country’s workforce. Development in the agricultural sector will enrich the rural development which will lead toward rural transformation and ultimately the desired structural transformation (Mogili and Deepak, 2018; Shah et al., 2019). The role of Artificial Intelligence (AI) has been obvious in the agricultural sector recently. The sector needs the impetus from AI based technology to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology.

Artificial Intelligence (AI), Machine Learning and Data Analytics seem to be invading all the fields to bring in efficiencies in those respective fields. However, Toxicology field seems to be lagging behind in these areas although there is a huge scope to use these advanced technologies.

AI, data analytics and machine learning can be used for creating toxicology databases on chemicals, predictive modelling and AI for risk assessment, amongst other applications. One such effort was carried out for use of AI in creating a BOT, a ToxBot, for conducting risk assessments on chemicals which helped in gaining lot of efficiencies. The ToxBotwhich was created helped in conducting risk assessments and reducing time for such risk assessments from48 hours to 15 minutes. This also ensured using a uniform database, consistent output and helping toxicologists in arriving at unform conclusions. To begin with, the sponsors need to be very clear about the objective and expected outcome of the AI solution. The first step in creating these AI solutions is creation of a datasbase. While, creating AI solutions for risk assessment two aspects are important- source of Toxicology thresholds and the other one to derive exposure to population to assess safety. Creating a Toxicology data base for deriving thresholds is tedious. Toxicology studies generate huge data on multiple chemicals, drugs and botanicals. However, these data are scattered, not uniformly documented, and there is lack of efforts to compile them. Each constituent has data on each end point and these data are spread across multiple species. Hence the Toxicology data on each constituent has variables such as species, routes, doses, duration and end points. The efforts start with creating a complete Toxicological profile including all available data on each end point in each species by specific route of administration. These profiles then form the basis of a data base, which then enable to derive safety thresholds. The other database or system is to derive the exposure. This depends on the type of product, daily dose, frequency, route, concentration of chemical and the target population. These can be populated through an exposure matrix. For Al, these data need to be documented in uniform manner for subjecting it to Machine Learning and creating AI solutions. The AI experts would then be required to understand the entire process of the desired outcome and how the dataare used, at what points. This helps them in creating an algorithm. Once the algorithm and coding is created, the product is ready to be tested.

At a compound annual growth rate (CAGR) of 7.6%, the global poultry industry expanded from $352.02 billion in 2022 to $378.84 billion in 2023. Global production of chicken meat was projected to reach 103.4 million metric tons in 2023, primarily as a result of rising output in China, India, Brazil, and the US. India is currently the world’s second-largest egg producer (138.3 billion eggs in 2022–2023), trailing only China. It is also the world’s fifth-largest broiler producer (producing 4.99 metric tons (MT) in 2022–2023), behind China, the United States, Brazil, and the Russian Federation.

Introduction

The term "artificial intelligence" was originally coined in 1956 at a conference hosted by Dartmouth College. While the aspirations and dreams of the attendees were sky-high, the reality was sobering. The thought of citizens in space or self-driving cars within a decade was ancient science fiction. The building blocks of the required computing infrastructure, as well as AI, notably deep learning and statistical machine learning methods, were not even in the distant future. The aim was to create an artificial brain for a machine, where the "brain" is the so-called "expert system", a software program that can integrate a large knowledge base and use it to solve problems in the same manner that a human expert could. The intellectual merits and achievements of a few AI systems designed solely to narrowly excel in a specific problem became the actual problem of the nascent field.

Millets have potential as both food and fodders crops. Foods made from millet are abundant in micronutrients and necessary amino acids for regulatory functions. Since, millets were traditionally grown under adverse conditions, a great amount of polymorphism was preserved, making millet crops naturally resistant to abiotic and biotic stresses. Using data-driven technology to increase genetic gain in breeding operations can improve traditional breeding. Artificial intelligence can be used to create varieties that are adapted to certain soil and climatic conditions. In order to achieve breeding goals, machine learning (ML) is crucial in data mining and analysis, providing information for decision-making. We have been able to generate information on interesting genes using data mining and allele identification methods. For the exploitation of the natural variation and the identification of target genes, advances in high-throughput sequencing for the detection of genetic variations as well as the role of phenomics in genomics-assisted breeding can be employed. QTLs and marker-trait correlations can be identified via genome-wide association analyses. Multiple loci and genes that control agronomic traits have can be studied by comparative and functional genomics. Model selection is crucial for ML algorithms in smart breeding, which depends on knowledge in genotype, phenotype, and G×E interaction to be handled through computer science.

9.1 Introduction

The increase in world’s population and respective food demand has brought the global concern. The climate change alone has put a tremendous burden on the farming communities, and has become the major challenge to feed the burgeoning population. It has been estimated that by the end of the year 2050, the global population will be around 10 billion, meanwhile, the food demand will also be increased by 70-100 %. The traditional method of agricultural practices are not sufficient enough to meet the rising food demand. Therefore, to meet the increasing food demand, agriculture is intensified by the use of chemical fertilizer, pesticides, herbicides, which deteriorat the soil health. Thus, new automated/ mechanised systems have been introduced into the agriculture sector to ensure precision farming. These systems are quick in action, and satisfing the requirements along with generation of billions of employment worldwide. In a developing country like India, 65-70 per cent of the total population resides in rural areas, of which, most of the households are dependent on agriculture. The agriculture sector in India accounts for 18 per cent of the country’s GDP, and provide employment to 50 per cent of the total work force. The agriculture sector is one of the most sensitive sectors of Indian economy which supports all other sectors, and spreading its benefit to the far-reaching areas. Therefore, it becomes important for the developing country like India to develop agriculture sector to boost-up the rural economy.

With the advent of technology, a rapid transformation has been occurred in various industries, while agriculture remains the least digitized. Though, the application of aircraft/ drones in agriculture for fertilizer/ pesticides applications, image capturing, access to real-time quality data has adopted in some developed countries like Australia, USA, Japan, and some of the European countries. Moreover, these unmanned aerial vehicles could provide data of soil health, monitor crop health, assist in irrigation scheduling, yield estimation, and weather data analysis too. However, such development in agriculture is still lacking in developing countries. The agriculture development in the modern era could be done by taking the biological information and agricultural knowledge through integration of modern agriculture information technology with traditional agricultural practices. These knowledge integrations with mobile internet, cloud computing, and machine learning could enable to control the agricultural production system over stressed climate condition easily. Thus, Artificial Intelligence (AI), which is a sort of machine intelligence, an inbuilt set of programme and algorithms similar to the human brain in the current world is gaining more attention in agricultural application for precision farming.

Artificial intelligence (AI) uses multiple technologies that equip machines to sense, comprehend, plan, act, and learn with human-like levels of intelligence. Fundamentally, AI systems perceive environments, recognize objects, contribute to decision making, solve complex problems, learn from past experiences, and imitate patterns. AI technologies such as deep learning allow computer systems to understand a problem, learn from examples, and make predictions.

The AI has a tremendous potential to solve the diverse application problems in the field of domestic animal breeding and management. Till date, most of the studies have been focused on animal behaviour (Valletta, et al., 2017), cognition (Lake et al., 2017), animal welfare (Neethirajan, 2021), individual identification (Norouzzadeh et al., 2018), growth or body condition scoring (Zin et al., 2020), disease detection and its monitoring (Kumar et al., 2021), precision farm management (Radun et al., 2021) etc. particularly in the pigs, poultry and cattle species. These AI based applications primarily involves data generation, data processing, use of gadgets / IoT devices, development of smart machine learning algorithms and their integration to attain precision to solve various issues in animal husbandry. Having said that, very few works have been carried out with respect to applications of AI in domestic animal breeding, and major problems like breed identification, genomics- based animal selection criteria and breeding decision, are yet to be automated. Moreover, there are some bottlenecks in application of AI in animal breeding, like the lower accuracy of a decision/outcome and large cost involved in creating AI based infrastructure.

Agriculture is the support for the sustainable health and economic growth of the country. With the frequent changes in climatic conditions and increase of global population, it is important to come up with new varieties and nutrient rich crops. In this context, underutilized and neglected millets, considered as “nutri-cereals” or “smart foods” due to better adaptation in diverse environmental stresses and tolerance to insect pests and diseases, also a staple crop for hunger-stricken areas. Millet production is limited by the lack of intensive farming and deployment of genetic tools for trait and nutrition improvement. The recent advancement in machine learning (ML) and Artificial Intelligence (AI) technologies are key to depict plant response to environmental perturbation in huge data sets. Therefore, precision agriculture or smart farming is necessary to explore the genetic makeup of the neglected millets. AI has a substantial potential to handle numerous challenges in the formation of knowledge-based farming systems. The next agricultural revolution would emphasize more on farm machinery using AI integrated with the Internet of Things (IoT). In Smart agriculture, deep learning facilitates fast and interesting data analysis and provides support to researchers and farmers by the outcomes generated from the analysis. Here, we emphasize on the advancement of AI and its application to monitor the underutilized and neglected millets quality, yield and disease assessment. This chapter summarizes how AI can contribute to the improvement of sustainable productivity and quality of underutilized and neglected millet.

Currently, the milk production of country is 221.06 million tonnes (2021 22) and the target is 300 million tonnes by 2023-24. The livestock sector contributed nearly 5.21% of total gross value added (GVA) and 28.36% of the agriculture and allied sectors GVA. Thus, there is a huge gap between demand and supply, therefore with the requirements of better yield from the dairy animals, we need more better and advanced options. Artificial intelligence (AI) is one of such options which can be exploited in dairy industry. Artificial Intelligence can be a game-changer for the dairy farming in India and it can emerge as a tool that empowers farmers in monitoring, forecasting as well as optimizing the farm animal growth. It can predominantly change the scenario of dairy farmers by maintaining the health, physiological and physical conditions of dairy-cows. This knowledge-based technology has a huge potential and could confront the loopholes in dairy farming and thus indirectly can strengthen the dairy industry. In dairy farming AI has multiple applications like monitoring the activities of the dairy-cows, boosting the milk production and farm productivity, detection of mastitis in dairy-cows and developing the smart cow houses powered by image analysis.

AI enables the dairy farmers to determine whether the cow is ill, ready to breed or has become less productive. The AI also sends alerts to the farmer about the change in the cow’s behavior allowing human intervention where needed. Regular 24x7 monitoring of dairy animals is the key for successful dairy farming, however, traditional methods of monitoring relied on visual observation, but it was not very efficient due to the lack of time and human resources. Without AI, it would be almost impossible for the farmer to keep a watchful eye on every cow in the herd. Artificial Intelligence components of the dairy automation system process the collected data to provide insights on the heat stress, change in feeding efficiency and the estrus of the cow. Diseases like sub-clinical mastitis, one of the more common diseases in the dairy industry, cost the Indian dairy industry one billion dollars annually. Eventually, it provides new hope and open prospects for the overall quality and progress in the dairy industry through a profitable business approach in dairy farming

What is Artificial Intelligence

‘Artificial’ is taken to mean a property of a human-made machine. ‘Intelligence’ will be that when something is able to perform a new action or change its state to achieve the optimal response it could perform given external inputs. This term is coined by- John McCarthy (American computer and cognitive scientist) during 1956 So, AI is basically a machine able to mimic human intelligence by having the ability to predict, classify, learn, plan, reason and perceive.

AI is an emerging technology in the field of agriculture. AI-based equipment and machines, has taken today’s agriculture system to a different level. This technology has enhanced crop production and improved real-time monitoring, harvesting, processing and marketing . The latest technologies of automated systems using agricultural robots and drones have made a tremendous contribution in the agro-based sector. Various hi-tech computer-based systems are designed to determine various important parameters like weed detection, yield detection and crop quality and many other techniques.

Artificial Intelligence (AI) technology is increasingly used in our everyday lives. It is used in a variety of industries from gaming, journalism/media, to finance, as well as in the research fields from water resources to robotics. In this chapter concept and applications of AI are discussed in brief.

6.1 Historical Background

After World War II, a number of people worked independently on intelligent machines. The English mathematician Alan Turing was the first who explained the concept of intelligent machines through a lecture in 1947. He stated that intelligent machines could be developed by programming computers rather than by building machines. Since late 1950 many researchers are working on intelligent machines, mostly on programming computers

The artificial intelligence (AI) world is quickly growing as it reaches several different industries. You currently will see AI used for many different purposes from agriculture to automotive; with the passage of time you will see even more of it. Agriculture is one of the most important branches of the IT industry. Agriculture is an important industry and is a big part of our economy’s base. In terms of annual sales, the agricultural industry contributes to our economy almost 17%.AI is becoming a technical advancement that boost and protects crop yield, as climate change and populations rise. Here in this chapter basics of Artificial Intelligence have been discussed how it can be related to agriculture.

1. Introduction

Artificial Intelligence has become most prominent in current generation. Every field is utilizing Artificial Intelligence for their better results and which also result in saving a lot of time. As Artificial intelligence is boosting different sectors to increase the productivity and efficiency and its utilization across different sectors like in the field of CIVIL Artificial Intelligence can be used to analyze the structures of buildings, dams etc., and Artificial Intelligence solutions are assisting in so many ways to overcome the traditional challenges in every field.

I. What is Artificial Intelligence

Artificial Intelligence is one of the most prominent field of Computer Science Engineering which attempts to redefine the tasks which are carried out by humans in their daily life with better results and maximum accuracy. Artificial Intelligence is actually meant to ease the work of human where the word artificial states the work done without any human interference and intelligence states the capabilities like human brain to perform tasks. The field artificial Intelligence has given so many definitions from time to time which can be typically stated as

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

Artificial intelligence is founded on the idea that human intelligence may be described in such a way that a computer can simply imitate it and carry out tasks ranging from simple to sophisticated. Artificial intelligence is assisting several agricultural sectors in increasing output and efficiency. In every industry, AI technologies are assisting in overcoming traditional hurdles.

AI intervention in agriculture is assisting farmers in increasing their farming efficiency while reducing harmful environmental impacts. The agriculture business has embraced AI with open arms in order to improve overall results. The way food is produced is changing as a result of artificial intelligence. Incorporating AI technology into agriculture aids in the control and management of any unwelcome natural occurrence.

1.1 Introduction

Since the beginning of humankind, we have thrived as a species due to our unique capabilities. These skills have developed and progressed over the centuries to the point that, in recent decades, we have been able to build machines that can imitate our intelligence, as far as we comprehend it. A novel type of world may lie ahead of us.

Artificial intelligence is the future, and the future is here." - Dave Waters

Artificial intelligence (AI) refers to the replication of human intelligence in computers, which are programmed to think and behave in a manner similar to humans. Literacy, logic, problem-solving, perception, and linguistic appreciation are all manifestations of cognitive abilities. Artificial Intelligence refers to the development of computer systems, computer-controlled robots, or software that are designed to exhibit intelligent behavior similar to that of the human mind. The term artificial intelligence is composed of the word "Artificial," which denotes something created by humans rather than existing naturally, and "Intelligence," which refers to the capacity to gain and utilize knowledge and skills.AI is achieved by the analysis of patterns in the human brain and the evaluation of cognitive processes. The culmination of these studies leads to the creation of sophisticated software and systems with advanced intelligence. The term AI was coined by McCarthy et al. 2006 while working on Dartmouth summer research project in which they proposed that machines have the capability to emulate "every facet of learning or any other characteristic of intelligence."

Abstract

Natural sunlight is the cheapest source available, but for horticulture it is not always attainable in sufficient quantities. Therefore, the use of artificial light has become very common in order to increase production and quality. Under artificial lighting technologies, LED lighting is one of the technology that can help in producing crops and flowers in a more effective and sustainable way around the world. The benefits of LEDs are that the spectrum and intensity can be selected and adjusted, the energy efficiency is higher than most conventional light sources and their small size, durability, long lifetime, cool emitting temperature, and the option to select specific wavelengths for a targeted plant response make LEDs more suitable for plant-based uses than many other light sources. These advantages, coupled with new developments in wavelength availability, light output, and energy conversion efficiency, can bring about a revolution in horticultural lighting. Light quality plays a major role in the appearance and productivity of ornamental and food specialty crop species. Far-red light, for example, is important for stimulating flowering of long-day plants (Deitzer et al., 1979; Downs, 1956) as well as for promoting internode elongation (Morgan and Smith, 1979). Blue light is important for phototropism (Blaauw and Blaauw-Jansen, 1970), for stomatal opening (Schwartz and Zeiger, 1984), and for inhibiting seedling growth on emergence of seedlings from a growth medium (Downs, 1956). The interactions are complex and continue to be unraveled at the molecular level (Devlin et al., 2007), but much of our understanding of these responses comes from studies with narrow-waveband lighting sources, in which LEDs provide obvious advantages. Thus, one potential role of LEDs in horticulture could be to enhance desired characteristics for specific crops. The artificial LED lightings increase the possibility of year-round production of ornamental plants, which will subsequently increase the income of growers related to ornamental industry. Light-emitting diodes pave the way for a better understanding of the interaction between light and pests, diseases, natural enemies and plants, as they make possible the use of very narrow wavelengths. LEDs as an option for reducing the use of growth retardants and obtaining better product quality in ornamental pot plants. Production of secondary metabolites increase with higher blue light ratio. Secondary metabolites production are increased and act against Reactive Oxygen Species (ROS) and pathogens, precondition the plant for environmental changes so they can cope with stress more efficiently.

12.1. Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the way biological neural networks in the human brain process information. They consist of interconnected nodes or “neurons” arranged in layers: an input layer, one or more hidden layers, and an output layer. Each neuron in the network processes data and passes the results to the next layer. ANNs are designed to recognize patterns, learn from data, and make predictions or classifications based on that learning.

In an ANN, the connections between neurons are weighted, meaning each connection has a strength or value that determines the influence of one neuron on another. Through a process called training, the network adjusts these weights to minimize errors in its predictions. This is typically done using techniques like backpropagation, where the error is propagated backward through the network to update the weights (Abiodunet al., 2018).

ANNs are highly versatile and have been applied across various domains, including image and speech recognition, natural language processing, and decision-making systems. Their ability to model complex relationships in large datasets makes them a crucial component in the field of machine learning and artificial intelligence (Ahmed et al., 2023).

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.

The artificial neural network (ANN), a data driven model, is inherently suited to the problems that are mathematically difficult to describe. An ANN is a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data sets, where explicit knowledge of the internal processes is not available. In this chapter general structure of ANN is described and also explained briefly with a case study.

7.1 What is Artificial Neural Network?

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous system, such as brain process the information. An ANN is an adaptive (Adaptive means that the system parameters are changed during operation i.e. training phase), most often nonlinear system that learns to perform a function (an input/output map) from data. In more practical term, neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find out patterns in the data.

Introduction

Global diversity in domestic animals and aquatic organisms is considered to be under threat worldwide. Worldwide 11 % birds, 25 % mammals and 34 % of fish species are threatened (Lovell-Badge, 2001; Vemuganti and Balasubramanian, 2002) and aquatic ecosystem is becoming more vulnerable to disasters due to run off from agriculture and industrial sources, oil spills or sudden environmental changes leading to total elimination of stocks from different ecosystems. The multifarious human activities in open waters have threatened the fish diversity all round the world. Fish worldwide are in crisis. The effective population sizes in fish hatcheries are getting smaller and smaller that causes loss to farmers worldwide. The World Resources Institute has reported that nearly 70 percent of the world’s marine fish stocks are over fished or are being fished at their biological limit. Apart from fish, across all live stock species there is a shrinking pool of genetic diversity. For instance, the effective population size for all dairy cattle breeds is less than 60 animals. To preserve the gene pool of unique populations by protecting them from extinction, loss of genetic variability (inbreeding) and dilution due to inter breeding with unrelated populations (hybridization) gene banks or germplasm repository can play a vital role. Germplasm repositories in fisheries could be used for: 1) restoration of threatened species, 2) brood stock improvement through genetic modification of a targeted population, and 4) information and technology development including gene pool database management.

The artificial seed production technique was first used in clonal propagation to cultivate somatic embryos placed into an artificial endosperm and constrained by an artificial seed coat. Today artificial seeds represent capsules with a gel envelope, which contains not only somatic embryos but also axillary and apical buds or stem and root segments (Vdovitchenko and Kuzovkina, 2011).

The prime requirements for preparation of artificial seeds are high frequency somatic embryogeniccalli, synchronous embryo development and maximum conversion of embryos into plants. Success in production of synthetic seeds mainly depends on how best callus development and plantlet regeneration are achieved. However, over time the morphogenetic competence of differentiated cultures declines (Lynch and Benson, 1991). Therefore, the primary goal of synthetic seed production is to produce somatic embryos that resemble more closely to the true seed embryo in storage and handling characteristics so that they can be utilized as a unit for clonal propagation and germplasm conservation. Synthetic seeds may be hydrated or dehydrated and may be quiescent or not. Encapsulation of micropropagules enables to satisfy the requirements of synthetic seed development. The gelling agents used for encapsulation for production of synthetic seeds act as protective cover. The encapsulated synthetic seeds also contain growth nutrients, plant growth promoting microorganisms (mycorrhizah, Rhizobium, etc.), and/or other biochemical constituents necessary for optimal embryo-to-plant development (Fig. 4.1). Fungicides may also be used in the liquid gelling medium to protect them during storage and germination in vitro and in vivo.

Introduction

1. The condition has been reported the world wide in growing broiler chickens.
2. The affected birds show clinical signs usually during 4 - 5 weeks of age. The clinically affected birds are smaller, listless with ruffled feathers, pale head and shrunken comb. Severely affected birds have distended abdomen, which restricts their normal movement. Death may occur suddenly, and not all the broilers which die of the condition have ascites. The incidence of the disease varies between 2 to 20%.

Ascorbic acid (vitamin C) is present in almost all fresh fruits and vegetables in varying quantities ranging from 0.02 to 1.0 mg per g fresh weight. It is most abundantly found in bittergourd and berries.

The method for estimation of ascorbic acid is given here below as described by Albrecht (1993). It is an easy and simple method based on titration technique.

Explant

The tissue obtained from plant to be cultured is known as Explant. It may include the parts of shoots, leaves, stems, roots, flowers and callus.

Maintenance of Aseptic Environment

All culture vessels, media and instruments used in handling tissues as well as the explants must be sterilized. The importance is to keep the air surface and floor free of dust. All operations are carried out in laminar air-flow, a sterile cabinet.In general, the methods of elimination of these sources of infection can be grouped under different categories of sterilization procedures: 

7.1. Introduction

In the modem lifestyle, people are extremely busy in their work and lack the time to cook their own foods. They mainly depend upon processed and packaged food for their nutritional intake. So it becomes essential that the packaged products should be safe, hygienic and shelf stable. To preserve the food for long duration, thermal processing is most suitable method. Aseptic packaging is a form of thermal food processing. Aseptic packaging can be defined as the filling of sterile containers with a commercially sterile product under sterile conditions and then sealing the containers so that reinfection is prevented; that is, they are hermetically sealed. 

Botany: The ash gourd(Benincasa hispida) belonging to the family cucurbitaceae is an annual creeping vine that can either climb structures or be allowed to spread out on the ground. This plant features large green leaves and thick stems covered with coarse hairs. The showy, golden yellow blooms appear early in the summer, and female flowers give way to round or spherical fruits. Young ash gourds are covered with a soft down that disappears with maturity. Fully matured gourds have a white, waxy coating covering the surface.

Field preparation:The field selected for seed production must not have been sown with ashguard in the previous season. This is done to avoid volunteer plants that cause admixture.After proper ploughing, at a spacing of 2.5 x 2 m distance take pits having 45 cm length, width and height. Ten days after that, apply 10 kg FYM and urea 30 g. Super phosphate 72 g and potash 19 g per pit. Then mix the above nutrients with soil and fill the pits and level them.

Soil and climate:Soil with neutral pH must be selected. Loam or clay loam soils are best suited. Higher organic matter will lead to production of vigorous seed.Seed is very sensitive to whether. Hence selecting the right season is necessary. Though ashgourd can grow through out the year, seed crop should be grown such that the seed matures in cool dry climate. This will facilitate proper ripening of fruits and reduce the pest and disease infection. Seasons are selected with this idea in mind. In

I. FILL UPS

1. Ashgourd is a _____ crop.
2. Stigma receptivity is for _____.
3. Ashgourd is a _____ pollinated crop.
4. Ashgourd fruit is a _____.
5. The isolation distance for ashgourd certified seed production is_____.

There has been a surge of data in every field of research due to advancements in information technology. Data collection and storage has become much easier and cheaper in recent years. This voluminous data needs to be systematically stored for quick retrieval, process and to find useful patterns. Data mining techniques help in deriving useful patterns from data. These techniques which are also called as machine learning techniques majorly categorized as classification, clustering and association rule mining methods. Classification is a supervised learning method which needs labeled dataset for model building and prediction purposes. Clustering and association rule mining methods do not require labeled dataset and hence they are referred as unsupervised learning methods.

Clustering aims at grouping the objects based on a set of measured variables where similar objects are assigned to one group. For example, let us say, if we have collected information on 50 fish pond parameters (50 variables) and we wish to find the groups in terms of pond management (like well managed to poorly managed), with cluster analysis, we can group the ponds based on pond variables. In the process of clustering, each pond is assigned to one of the groups made. The question is how many natural clusters present in the data as in many cases we would not be getting perfect clusters of disjoint sets. We can achieve better accuracies in cluster formation only by trial and error. Good clustering criteria are to see that there is minimum with-in cluster variations and maximum between cluster variations.

Cluster analysis has several applications in fisheries research. It could be used to group fish stocks from different regions, categorise ponds based on their technical efficiencies, identify the farm clusters with high disease risk, group farmers based on their preferences, agro zoning based on climatic variables and culture practices. Cluster analysis is also applied in fish bioinformatics to group the similar sequences.

Botany

Ashwagandha (Withania somnifera), also known as Indian ginseng belonging to the family Solanaceae, is an important ancient plant, the roots of which have been employed in Indian traditional systems of medicine, Ayurveda and Unani to cure asthma, fever, chest complaints. It is an erect branching undershrub reaching about 1.50 m in height. It grows in dry and sub-tropical regions. Being hardy and drought tolerant species with its enormous biocompounds, its usage is forever regarded and continuous to enjoy the monopoly in many parts of India, particularly in Madhya Pradesh. It grows in dry parts in sub-tropical regions. Rajasthan, Punjab, Haryana, Uttar Pradesh, Gujarat, Maharashtra and Madhya Pradesh are the major Ashwagandha producing states of the country. Roots are fleshy, tapering, whitish brown. Leaves are ovate and flowers are greenish. The mature fruits are orange-red berries.

Varieties:Poshita and Rakshita are high yielding varieties released by CSIRCIMAP, Lucknow. Jawahar 20 is cultivated in Madhya Pradesh. WSR is another variety released by CSIR-Regional Research Laboratory, Jammu. Nagori is a local variety with starchy roots

4.1. Origin and distribution

• It is called as Indian ginseng.

• Native of Africa, India and Mediterranean region.

• Wild in grazing grounds in Mandsaur and forest lands in Bastar district of Madhya Pradesh, all over foothills of Punjab and Himachal Pradesh and Western Uttar Pradesh, in Himalayas.

• Also wild in Mediterranean regions in North Africa.

4.2. Economic importance

Improved Varieties of Asparagus

(1) Perfection

Source : Developed at IARI, New Delhi

Maturity : Early

Spears : Large, green, succulent, heavy cropper and uniform in maturity

Production Technology of Asparagus

Climate

Asparagus is temperate season vegetable and bears excellent crop between a temperature range of 15-24ºC for most of the growing season. The ideal day temperature is 25-30^oC and night temperature 15-20^oC. It can tolerate frost up to some extent but continuous freezing is harmful for its growth. Spear initiation occurs at soil temperature of more than 10ºC. Spear elongation is faster at high air temperature. Root and shoot development is reduced below 13ºC and above 29ºC. Due to warm weather, spear tips open prematurely and quality of spears is reduced. Elevation of more than 1000m is good for high yield.

Asparagus is an herbaceous perennial that is widely grown in the United States, Germany, Spain, and France, with India contributing only a little amount. For many years, asparagus has been grown. This was a favourite crop of the Ancient Greeks and Romans. It belongs to the Liliaceae family and is thought to have originated in the eastern Mediterranean and further east in the Caucasus Mountains. It’s a monocotyledonous perennial that’s farmed for its edible stems (spears).

Introduction

Mushrooms are very nutritious products that can be generated from lignocellulosic waste materials; and are in rich in crude fibrend protein. In fact, mushrooms also contain low fat, low calories and good vitamins. In addition, many mushrooms possess multi-functional medicinal properties. Edible mushrooms once called the “food of the gods” and still treated as a garnish or delicacy can be taken regularly as part of the human diet or be treated as healthy food or as functional food. The extractable products from medicinal mushrooms, designed to supplement the human diet not as regular food, but as the enhancement of health and fitness, can be classified into the category of dietary supplements/mushroom nutriceuticals (Chang and Buswell, 1996).

According to Indian Philosophy (Bhat, 2012) the physical body is called Annamaya Kosha.A body, when well nourished grows well by absorbing the energies from the food consumed. Fasting is another aspect of traditional food system. It is meant to purify the mind and the body.

Introduction

This is a disease mainly of the respiratory system affecting domestic poultry, wild birds and zoo birds. Systemic aspergillosis affecting visceral organs and other tissues of the bird’s body is also reported. Reports are also available on eye and brain infections.

Assam is surrounded by Bhutan and Arunachal Pradesh on the North; Nagaland and Manipur on the East; Meghalaya and Mizoram on the South and Bangladesh, Tripura and West Bengal on the West. The total land area of Assam is 78,523 sq. km and it accounts for 2.4% of total geographical area of the country. Demographically, Assam is the most populous state in the North East India. Economically, out of eight North East states, Assam is the most developed one, but compared to other states like Maharastra, Tamilnadu, Gujrat, it lags far behind in almost all economic spheres.

Antibiotics are produced by some true bacteria, actinomycetes and fungi that destroy or inhibit the growth of other microorganisms. Some antibiotics are synthesized or modified in the laboratory; however, their origins are always in living cells.

9.1 Assay of Antibiotics

Soil serves as the major repository of microorganisms some of which produces antibiotics. Four groups of soil microorganisms viz; Streptomyces, Bacillus, Penicillium and Cephalosporium represent three microbial types, namely, actinomycetes, true bacteria and molds respectively.

Following procedure may be applied to isolate antibiotic-producing microorganisms from soil:

Isolation and Purification of Secondary Metabolites

Isolation

The act of isolating something; setting something apart from others.

Purification

The process of making pure, free from anything that debases, pollutes, or contaminates, the process of removing impurities.

Introduction

The characterization of the potential health impact of engineered materials produced through nanotechnology is an emerging issue of considerable discussion and debate. The current focus on nanotoxicity fails to assess the risk in different local environments and populations. People in developing countries may be more prone to adverse effects of nanoparticles because of underlying health conditions and malnutrition. Moreover, genetic susceptibility to toxic effects varies in diverse ethnic groups and geographical areas. The scientific community needs to identify these information gaps before developing regulations and standard methodologies for nanotoxicity assessment.

Need for systematic mapping and hazard zonation is emphasized in this chapter. Though prevention of natural disaster is not possible, its destruction could be minimized through implementation of effective hazard management system. With this motive, the coastal region between Tangapatnam and Ovari, Tamilnadu State has been classified into various categories of vulnerability based on its response to the 26th December 2004 tsunami. Based on the extent of seawater inundation and coastal geomorphic features, we have assessed the tsunami impact and estimated the behavior of the beaches in case of similar havoc in future. This study had shown that the maximum seawater inundation recorded in the study area is 750m in Colachel and the minimum is 100m in Kadiapatanam, Mandakadu and Vaniakudy areas. Chinnamuttom, Kanyakumari, Manakudy, Pallam and Colachel areas are under high risk.

INTRODUCTION

On 26^th December 2004, tectonic disturbances happened near Sumatra Island with an intensity of 9.3 in the Richter scale wrecked havoc on southwest part of India. In addition to inflicting life and infrastructure loss, the resultant tsunami event had also eroded severely the coastal region and modified topography of beach and shallow coastal regions. It did raise the concern of scientists and emergency planners about the impact of larger earthquake and consequent tsunami should there be any recurrence of such event. Despite becoming aware of tsunami hazard, there has been confusion about areas at risk and areas of safety. This chapter is an attempt to assess the vulnerability of part of Tamilnadu coast based on its geomorphological conditions and the impact of recent tsunami. The hazard maps have been generated and are intended for educational purposes, to improve awareness of tsunami hazards and to encourage responsible emergency planning efforts by illustrating the range of possible tsunami events.

Abstract

Higher temperature and altered rainfall patterns accompanied by extreme weather events are being considered indications of climate change. These factors impact vegetation growth in natural forest, open scrub and in agriculture land. Vegetation index based on satellite data was used to analyse agricultural vulnerability in India to understand impact of rainfall variability. Time-series Normalized Difference Vegetation Index (NDVI) data products obtained from AVHRR (1982 to 2006) and MODIS (2000-2011) were analyzed to assess spatial and temporal variability. Coefficient of variation of maximum NDVI was assessed for the study period and results were corroborated with Standard Precipitation Index (SPI) instead of actual rainfall. Resolution of AVHRR time-series data facilitated study at regional-scale while MODIS datasets with 250m pixel resolution helped in identifying vulnerability at district-level. Study indicated that over 241 Mha areas in the country may not be vulnerable to rainfall variability induced climate change while over 81.3 Mha in arid, semi-arid and dry sub- humid regions may be vulnerable. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severally vulnerable while 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Study indicated that length of cropping season was decreasing due to delay in start of season. The paper also presents analysis of variations in SPI and resultant NDVI in typical drought and flood years in the country.

Keywords: MODIS, AVHRR, NDVI, Rainf ed Agricul ture, Vulnerability, Climate Change

Abstract

The paper deals with taxonomy, ethnobotany and threat status of lesser known liana species Hodgsonia heteroclita (Roxb.) Hook.f. & Thomson. The local people use in terms of foods and rites and its role in ecosystem have been reported. Local people used the seeds of liana as food because of high nutritive value. Threat assessment has been following IUCN version 3.1 to evaluate the threat status of the species in Sikkim Himalaya. Out of the five criteria, data on two criteria could be collected and was used to evaluate the threat category of the species. The criteria used for threat assessment were (a) reduction in population and (b) geographical range in the form of either B1 (extent of occurrence) or B2 (area of occupancy) or both. Considering the population status and geographical range of the species, it has been classified as “Least Concern” (A1acd; B2ab(ii,iv)).

Concept: It’s a natural process that modifies the cytology, physiology, biochemistry, and physical characteristics of seeds. These modifications lower viability and ultimately result in the seed’s death. A decline in the proportion of germination indicates degradation, and weak seedlings are produced by seeds that do germinate. The factors that impact the ability of seed to be stored include the seed’s quality at the time of storage, its pre-storage history (environmental factors during pre- and post-harvest stages), its moisture content or ambient relative humidity, the temperature of the storage environment, the amount of time the seed is stored, and biotic agents. Seed will inevitably get damaged when being stored (Balesevic-Tubic et al., 2005). The quality of the seed at the time of storage, its pre-storage history (environmental factors during pre and post-harvest stages), its moisture content or ambient relative humidity, the temperature of the storage environment, the length of time the seed is stored, and biotic agents are factors that affect the seed’s ability to be stored. Different physical changes can happen to seed during storage because of factors like temperature, moisture content, and length of storage. The viability and vigor of the seeds may be impacted by these modifications.

INTRODUCTION

Heterotrophic organisms, such as zooplankton, use phytoplankton as food, regenerating nutrients through metabolic processes and transferring energy to higher trophic levels. Zooplankton are involved in the conversion of waste matter into animal food that may be consumed, in addition to helping to shift food from the primary to the secondary level. These species act as a bridge in the food chain, carrying energy from the primary producers, planktonic algae, to the larger predatory invertebrates and fish that eat them (Kumari et al., 2008). Macróplankton (200–2000 μm), microplankton (20–200 μm), nanoplankton (2–20 μm), picoplankton (0.2-2 μm), and femtoplankton (0.02 0.2 μm) are the different sizes of plankton (Kumar et al., 2023).

The sensitivity of zooplankton to alterations in aquatic environments is significant. Differences in species composition, abundance, and distribution of body size can be used to identify the consequences of environmental disturbances(Joshi & Joshi, 2011). A number of variables, including temperature, sunlight, ocean currents, and nutrition availability, affect plankton populations. They are vital to ecosystems because they support carbon cycling, global biodiversity, and the general health of the oceans (Cottenie et al., 2001). An evaluation of the pollution state heavily depends on the interaction between phytoplankton diversity and environmental conditions. The diversity of algae found in the Chlorophyceae family is a key sign of the quality of the water. The differences in water quality can be attributed to a combination of natural and human-caused factors. Owing to extensive human activity, anthropogenic inputs from various sources are typically the main variables influencing the water quality of the majority of rivers, lakes, estuaries, and seas, particularly those that are next to densely populated areas(Jeyaraj et al., 2016). Living things, including plants, plankton, animals, and bacteria, are known as bioindicators, and they are used to monitor the state of the environment's natural ecosystem. They are known across the world as aquatic pollution indicator species. Every organic entity in a biological system gives information about the state of its surroundings. For example, plankton responds quickly to changes in the environment and is a valuable biomarker for determining the quality of water as well as a sign of pollution. Plankton is the best indicator of the health of aquatic flora and serves as an early warning system(Wadjikar et al., 2017). An ecosystem that is in balance is one in which beneficial interactions occur between the environment and living organisms. Since water quality is essential to maintaining an environment in balance, it is evident that it plays a crucial role in this relationship(Jeyaraj et al., 2016).