eChapters

All across the world, the dairy industry is facing numerous problems and changes. Dairy development has received a considerable boost from factors including access to technical advancements, improvements in infrastructural facilities, and the development of modern marketing insights. It is interesting to see how the dairy sector is changing globally.

Operation Flood and dairy cooperatives became India’s greatest rural employment programme, enabling the modernization of the dairy industry to a point from where it may take off to fulfill the need for milk and milk products within the nation as well as chances on the international market. An unorganized sector handles 80% of the milk a rural producer produces, and an organized sector handles the remaining 20%. It has been discovered that dairy cooperatives significantly contribute to reducing rural poverty by increasing rural milk production and marketing.

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

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.

Traditional livestock diagnosis is often reactive and may identify disease only after clinical symptoms become visible. This chapter introduces deep-learning methods for automated health monitoring, early disease detection and predictive analytics. Models analyse images, videos, body temperature, heart rate, activity patterns and historical health information. Convolutional neural networks extract visual features and classify diseases, while LSTM networks identify patterns in time-dependent physiological and behavioural data. Multimodal fusion improves the reliability of predictions by integrating different data sources. IoT-connected systems can generate real-time warnings and support timely intervention. The chapter compares deep learning with conventional approaches and evaluates accuracy, recall, false-negative rates and practical deployment.

Introduction

Any work condition revolves around three main components - the man, machine and environment. 'Man'-is represented by the worker who is either the controller or the executor of an action. Therefore the work output depends on how efficiently the workers are performing. In this context, the health of the worker is of utmost importance. It is one of the prime supporting pillars of productivity. If the health condition declines, efficiency is reduced and productivity suffers. Work involves a variety of physical and cognitive activities performed to achieve a desired or specified productivity. It may include a single individual or a small or large group of people as the worker. It is characterized by a specified duration, specific speed or work pace and a required amount of effort. The performance and efficiency expected from the workers have a direct bearing on the work process or work tool and the work environment. All these stress the bodily systems and affect the health of the worker. The stress could be classified as physiological stress and psychological stress Occupational health deals with the study and maintenance of physical, mental and social well-being of workers in the workplace (WHO, 1957). Occupational Physiology and Ergonomics are related branches of Occupational health that are in particular concerned with the study and maintenance of health and well-being during work.

A large population of endocrine cells secrete the same chemical messengers (bio-active animals and regulatory peptides) as do the neurons and therefore these endocrine dells are referred as “Paraneurons”.
However these messengers are not used as neurotransmitters, rather they are released into blood circulation and interstitial fluid as hormones/chemical mediators to act on the target cells in endocrine or paracrine manner. This subset of endocrine cells forms the “Neuroendocrine system” and its synonyms are the “diffuse neuroendocrine system (DNES)”, “APUD (amine precursor uptake and decarboxylation) cell system” and the “regulatory peptide secreting cell system”. The DNES consists of cell populations scattered throughout the body either to form a coherent tissue in some organs (e.g. anterior pituitary, pancreatic islets, adrenal medulla) or to form a diffuse scatter of single cells or group of 2-3 cells within the stroma of the other organs (e.g. C cells of thyroid, intra-epithelial endocrine cells of the gut, airway and urinogenital system, chief cells of parathyroid, glomus cells of carotid body, melanocytes of skin, some cells of pineal gland and placenta, modified cardiac myocytes, hypothalamic neurons synthesizing oxytocin and vasopressin as well as neurons synthesizing releasing factors controlling the secretion of hormones by the adenohyposis etc.). These cells synthesize and store the hormones in their secretion granules as neurosecretory granules which have characteristic ultrastructural morphology.
The cells of the DNES can be identified by: (i) the ultrastructural morphology of the cells and their secretion granules, (ii) the empirical histochemical staining techniques which stain the neurosecretory granules, (iii) the novel current day, immunohistochemical techniques, or more aptly (iv) a combination of

Vero (monkey, African green, kidney) 
Morphology: Fibroblast
Species:, African green monkey 
Tissue: Kidney

Properties: Virology, virus titration, virus replication, plaque assays, bacterial cytotoxicity

Introduction

Flowering plants (Angiosperms) are identified by their morphological characteristics like flower, fruit and seeds that help their taxonomic characterization into different species, genera, families and orders. The floral characters which are used for taxonomic characterization of plants include the type of inflorescences, perianth structure, floral symmetry, arrangement of floral leaves, type of androecium, stamens, gynoecium, carpel, ovule, etc. The type of fruit and seed also play important roles in taxonomic classification. In this chapter we will briefly discuss about various morphological features of flowering plants (in botanical terms) that are useful in taxonomy.

Nomenclature of Plant

Nomenclature of plant is the formal and scientific naming of plants. It is related to but distinct from taxonomy for grouping and classifying plants and provide botanical name.

The scientific/botanical nomenclature started with Species Plantarum by Carolus Linnaeus in 1753. Botanical nomenclature is governed by the International Code of Nomenclature for algae, fungi, and plants (ICN), which replaces the International Code of Botanical Nomenclature (ICBN).

ICBN and ICN

International Code of Botanical Nomenclature, (ICBN) was adopted by the VIIth International Botanical Congress held in Stockholm, July1950. While the International Code of Nomenclature for algae, fungi, and plants (ICN) was adopted by the XVIIIth International Botanical Congress held in Melbourne, Australia in July 2011.

A scientific experiment is a planned research to obtain new facts or to confirm or refute the results of previous experiments. Research is usually conducted in a controlled environment to study the effect of one or more variables on observations. The objective of research may be to answer a question, verify a hypothesis, or estimate the effect of an independent variable on a dependent variable. After stating the research problem, we need to outline the methodology (or materials and methods) of the research, which includes developing an experimental design.

Scientific experiments are conducted to study the causal relationships between independent variable(s) and dependent variable(s). The researcher manipulates one or more independent variables and measures their effect on one or more dependent variables. Designing an experiment is planning an experiment. It is a complete sequence of steps taken before experimenting to ensure that appropriate data are obtained to perform a data analysis to get valid inferences about the problem. It must be seen that the data collected are relevant to the problem investigated. E.g., studying the effect of breed, age, feed, etc. on milk output. Here, the objects like breed, age, feed, etc. are called treatments. That is, any specific experimental conditions (like the addition of feed additives, new feed formula, etc.) applied to the experimental units are called treatments. The basic objects on which the experiment is done are called experimental units (E.g., a bird, an insect, an animal). However, the responses in all the experimental units receiving the same treatments may not be similar, due to the inherent differences in the experimental units, called experimental error, caused by extraneous factors.

Abstract

Training is essential but expensive. Hence, training impact is a crucial measure. National Agricultural Research System (NARS), India employs about 30000 scientists. To effectively manage such huge scientific-capital, Indian Council of Agricultural Research (ICAR) establishedCentres for Advanced Faculty Training (CAFT) and National Academy of Agricultural Research Management (NAARM). CAFT train faculty of State Agricultural Universities (SAUs), while NAARM trains scientists of ICAR institutes and SAUs. Assessing impact of these training programmes becomes essential. Application of knowledge, skills and attitude (KSA) acquired during training back at job environment is training-transfer that addresses learning, behaviour and results levels of training impact. This chapter critically analyses training-transfer of NAARM and CAFT programmes, suggests empirical framework to enhance impact of NAARM and CAFT programmes and thus contributes to literature. Policy strategies suggested in this paper hold wider applicability.

1. Introduction

With the advent of globalization and subsequent economic liberalization, the focus of Indian agriculture is shifting from “subsistence” to “commercial” mode, where farming is viewed as a “business” rather than “life-support” system of poor farmers. Enabling farmers “decision-making” capacity is a primary driver of success in the market-oriented agriculture. This “commercial focus” has brought tremendous changes in the “nature” and “mode” of extension work. On the other hand, extension work organizations in developing countries like India, are being impacted by sharp decline in public sector funding, rapid technological innovations especially in the Information and Communication Technologies (ICTs), and the shift to a knowledge-based workforce. These developments created new opportunities for the extension workers to perform the information dissemination, capacity building and technology application more effectively. 

Principle

Adenoviral, oncoretroviral, and lentiviral vectors are commonly utilised for gene delivery in mammalian cell culture and in vivo. Additional important examples of viral gene transfer involve baculovirus and vaccinia virus vectors. Stable cell lines produced with lentivirus technology provide consistent, long-term protein expression compared to the short-term expression achieved through transient transfection. This chapter focuses on cell transformation by lentiviruses.

Equipment

a) Biosafety cabinet level 2 (B2)

b) Carbon Dioxide (CO2 ) Incubator (set to 37°C with a 5% CO2 in air atmosphere)

c) Refrigerated centrifuge

d) Refrigerator

e) Inverted Microscope

f) Micro pipettes (1-10 μl, 10-200 μl, 100-1000 μl)

g) -86°C/-80°C deep freezer h) Autoclave

i) Hot air oven

j) Filtration Assembly

New varieties of bougainvillea have been developed in India at different research institutions, nurseries, botanical societies and also by individual efforts. Details have been mentioned above. New varieties developed at CSIR-NBRI need special mention. 

CSIR-NBRI has developed a number of new varieties by using various available breeding tools viz. through spontaneous mutation, classical breeding, chromosome manipulation and induced mutation (Banerji 2007, 2008, Banerji and Datta 1986, 1988, 1993, 1996, Banerji et al., 1987, Data and Banerji 1990, 1994a,b, Datta 1992b, 2004, Nath et al., 1983, Ohri 1995, Sharma 1996, Zadoo et al., 1975b, Jayanti et al., 2007, Roy et al., 2015).

The Central Nervous System not only consists of the Brain but also involves the Spinal cord.

The spinal cord is responsible for conveying information from the Brain to the rest of the body via the peripheral nervous system. The spinal cord can be divided into 4 functional segments.

1. C1-C5 (Upper Cervical Segments)

2. C6-T2 (Brachial Plexus)

3. T3-L3 (Thoraco - Lumbar)

4. L4-S3 (Lumbo-Sacral Plexus )

It is important to note that the motor and sensory function of the pelvic limbs are not only controlled by the Lumbo-Sacral plexus but also involve the Thoraco Lumbar segments (T3-L3) of the spinal cord. It is important to understand not only the neuroanatomy, but also the functional anatomy before approaching a neurological patient.

Introduction

Diagnostic biochemistry, is a branch of laboratory medicine that focuses on the analysis of bodily fluids to diagnose and monitor diseases. This field plays a crucial role in understanding the biochemical mechanisms underlying various conditions, enabling accurate diagnosis, prognosis, and management. The biological samples for diagnosis include blood, urine, cerebrospinal fluid, pleural fluid and tissues.

Factors Affecting Serum Enzyme Activity or Protein Level

1. Organ Mass and Enzyme Tissue Concentration:

• Higher enzyme concentrations in organs can lead to greater increases in serum enzyme activity during disease. For instance, the liver’s large mass contributes to potential increases in serum ALT activity.

2. Cell Location

• The proximity of cellular enzymes to the blood, urine, or other fluids determines the fluid in which an enzyme’s activity will increase upon cell injury. For example, renal tubular gamma-glutamyltransferase (GGT) is released into urine, not blood, following injury.

Body needs energy for the functioning of various processes for the nutrition and repair of damage tissue. This energy comes from the food nutrients. The food nutrients undergo a series of chemical reactions in the body to release the necessary energy.

The higher animals are dependent on plants and other animals for their nutrients. They can classified into two groups.

Carnivores : Those animals which obtain most of their food by eating other animals are carnivores in these species digestion is mainly enzymatic in nature and microbial digestion is minimal.

Herbivores : These animals obtain most of their food nutrients from herbage and other plant parts. In the herbivores, microbial digestion is of great significance and allows utilization of plant materials that are not broken down by the enzymes present in the digestive secretions.

Most of the domesticated animals except cat & dog fall in the category of herbivores. The domesticated herbivores can be divided into three groups:

1. Ruminants : Those possessing a rumen (cow, buffalo, sheep, goat, camel etc.) in which there is extensive microbial fermentation of the herbage diet in a specialized region of the digestive tract, prior to its digestion by the alimentary enzymes.
2. Psedoruminants eg members of the camelidae family eg camel, Ilama, alpaca, vicuna etc. In pseudoruminants animal third compartment of the compound stomach is absent.
3. Non-Ruminant : The second group is those with simple stomachs (Horse, Pig) in which fermentation takes place in the posterior part of the digestive tract.

Introduction

Digestion may be defined as the hydrolytic process carried out by various enzymes for breaking down complex food components such as proteins, fats and carbohydrates into simpler molecules like amino acids, fatty acids and monosaccharides, respectively. Whereas, absorption may be defined as the movement ofthese simple soluble digested food componentsalongwithvitamins, minerals and water from gastrointestinal track into the circulatory system.

The process of digestion starts from oral cavity (mouth) and ends at large intestineincluding tongue, teeth, jaws,secretions from salivaryglands (parotids, sub maxillary or submandibular and sublingual glands), pharynx, esophagus, stomach, small intestine and digestive enzymes secreted from pancreas and liver.