eChapters

Digital dairy farming integrates IoT, artificial intelligence, machine learning, big-data analytics, cloud computing, sensors and automation to improve herd management. This chapter explains how wearable devices and monitoring systems support early disease detection, welfare assessment, milk-yield analysis and precision feeding. Automated milking, feeding and other operations reduce manual labour, improve consistency and lower operating costs. The architecture of smart dairy systems—including sensors, wireless communication and decision algorithms—is examined alongside environmental and economic benefits. Barriers include high initial investment, limited digital skills, poor rural connectivity and cybersecurity risks. Future developments may include AI-enabled autonomous dairy farms and interconnected, sustainable agricultural ecosystems.

Robotics is transforming livestock production by automating repetitive, labour-intensive and precision-dependent operations. This chapter examines automated milking, robotic feeding, manure removal, health monitoring, drones and autonomous herd-management systems across dairy, poultry, swine and sheep production. Integration with AI, IoT and sensors creates connected environments capable of continuous monitoring and data-driven action. Benefits include higher productivity, lower labour requirements, timely animal care and more efficient resource use. Challenges include substantial investment, technical complexity, maintenance and farmer acceptance. Emerging developments such as autonomous farms, swarm robotics, solar-powered equipment and climate-smart robotic systems may further strengthen animal welfare, environmental sustainability and global food security.

• Epidemiological definitions

 • List of tests for international trade

 • OIE listed diseases

 • Solutions and reagents

 • Age and body weight of animals

 • Rectal temperature of animals

 • Normal values of blood vascular system

 • Vaccination schedule

Heart of bovine, caprine, equine, canine and feline is a four chambered organ having left and right atrium, left and right ventricles, mitral valve, tricuspid valve, aortic valve, pulmonary valve, arteries, arterioles, veins and venules. Heart starts its functioning even before birth and continues till death. Its pumping action helps in providing nutrients as well as oxygen to all organs and parts of the body and to remove the waste products of the metabolism from the different organs of the body. Malfunctioning of the heart (pumping action and /or current generation and conduction) adversely affects nutrient and oxygen availability to organs and removal of waste products from the body lowering the quality of life and making the survival reasonably difficult. Heart functioning can be examined through physical examination of the heart such as general inspection (looking for abducted elbow, jugular distension or pulse, edema of ventral abdomen or limbs, tachypnea /dyspnea), evaluations of vitals (temperature, pulse, respiration,

1. Introduction: Setting the Stage for GLP in Artificial Insemination
Artificial Insemination (AI) stands as a cornerstone technology in modern animal agriculture, playing a pivotal role in the genetic enhancement of livestock across a diverse range of species. This technique, particularly prevalent in cattle and swine, allows for the widespread dissemination of superior genetic traits from carefully selected sires, leading to significant improvements in productivity, such as increased milk yield in dairy cattle and enhanced growth rates in beef cattle. Beyond genetic advancement, AI offers considerable advantages in disease management by mitigating the risks associated with natural mating, thereby controlling the transmission of reproductive tract infections. Furthermore, AI provides economic benefits to livestock producers by reducing the need to maintain live bulls, which require substantial resources, and by offering access to a broader spectrum of genetics than would be feasible through natural service alone. The application of AI is not limited to traditional livestock; it is increasingly employed in the breeding of horses and smaller ruminants, as well as in the management of pet populations, indicating its growing importance in animal reproduction across various sectors. The expanding scope of AI underscores the necessity for robust quality control measures to ensure its efficacy and safety across different animal species and production systems. The success of any AI program is intrinsically linked to the quality of the semen used, making the operational standards of AI laboratories paramount. Optimal conception rates rely on semen with high viability, motility, and morphological integrity, which can be compromised by inadequate handling and unsanitary 130 Concepts of Good Laboratory Practices (GLP) conditions during collection, processing, storage, and insemination. Poor semen management can lead to reduced fertility and the potential spread of diseases, thereby undermining the benefits of AI. To ensure consistent and successful outcomes, standardized procedures throughout the AI process are essential, providing a framework for optimizing techniques and minimizing the risk of errors. In this context, Good Laboratory Practices (GLP) emerges as a critical quality paradigm. Defined as a managerial quality control system, GLP encompasses the organizational processes and the conditions under which non-clinical health and environmental safety studies are planned, performed, monitored, recorded, archived, and reported. While traditionally applied to the safety testing of chemicals and pharmaceuticals, the core principles of GLP, with their emphasis on systematic processes, controlled conditions, and rigorous data management, hold significant relevance for ensuring the quality and reliability of operations within animal AI laboratories. The genesis of GLP regulations in response to concerns about the integrity and consistency of scientific data further underscores its fundamental importance in any laboratory setting where the trustworthiness of results is paramount. This report aims to provide a comprehensive analysis of the GLP paradigm within the context of animal AI laboratories. Its objectives include defining GLP and its core principles, investigating the specific application of these principles in the various stages of animal AI, exploring the benefits and challenges associated with GLP implementation, reviewing relevant regulatory guidelines and standards across different regions, analyzing the impact of GLP adherence on AI techniques and outcomes, identifying effective data representation strategies, comparing the operational differences between GLP and non-GLP compliant laboratories, and discussing emerging trends and future perspectives on the role of GLP in advancing the field.

Physiotherapy may be defined as the use of physical techniques like massage, exercise, heat, cold, sound waves, electric current etc for the treatment of injuries and movement dysfunction. Physiotherapy deals with the treatment of diseases by physical methods. It is used for treating injuries, strain, spasm, painful conditions, sprain of tendons, paralysis, postoperative trauma etc. It improves functioning of nerve, muscles and bones. The aim of physical therapy is the restoration of function and promotion of tissue healing by assisting normal physiological processes. The physiological response to physical therapy is its effect on the vascular supply; this in turn reproduces similar changes in deeper tissues.

Fertility is the ability of a male to impregnate the fertile female. The reduced or temporary loss of fertility is called infertility whereas permanent loss of fertility is called sterility. A male with low libido or poor sex drive may have excellent semen quality or a male with good libido may have poor seminal quality. So, libido has no direct relation with the male fertility. Male infertility can be categorized into Impotentia coeundi and Impotentia generandi. 1. Impotentia Coeundi • The condition of reduced or lack of sex drive or Inability to copulate (failure of normal service) is referred to as impotentia coeundi. • It may occur due to immaturity, poor experience to perform coitus, inability or unwillingness to mount, inability to perform intromission or failure of ejaculation. • The following conditions may reflect the immaturity or poor/bad experience of the male animal which leads to impotentia coeundi:

Carnivora is an order of placental mammals that have specialized in primarily eating flesh, whose members are formally referred to as carnivorans. The order Carnivora is the fifth largest order of mammals, comprising at least 279 species. They come in a very large array of different body plans with a wide diversity of shapes and sizes. The common characteristics of carnivores are given below:

• They are meat eaters.

• There is the presence of canine teeth.

• The intestinal tract is short and is adapted to rapid digestion as well as the assimilation of meat.

• All species have anal glands. Some species, like the striped skunk (Mephitis mephitis), may eject the contents of the anal gland as a defensive maneuver.

• Os penis is present in males.

• There is a lack of a clavicle which helps in the freedom of movement of the forelimb.

• Ulna is well developed.

• Toes end in claws.

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.

Cloud computing and Edge AI provide complementary infrastructure for intelligent livestock management. Cloud platforms offer scalable storage, centralized analytics, model training and integration of data from sensors, cameras, automated feeders and farm-information systems. Edge AI processes information close to the farm, reducing latency, bandwidth consumption and response time. This chapter explains their architectures, workflows and applications in dairy, poultry, swine and small-ruminant production. Uses include disease detection, estrus prediction, behavioural monitoring, feeding and environmental control. Hybrid cloud-edge systems balance centralized intelligence with local autonomy. Challenges involving connectivity, interoperability, cybersecurity, cost, model reliability and ethical animal surveillance are also critically examined.

2.1. Classification of Fish

Fish are incredibly diverse aquatic vertebrates. There are over 32,000 de scribed species of bony fish, over 1,100 species of cartilaginous fish, and over 100 hagfish and lampreys. Fish can be broadly classified into three groups:

2.1.1. Superclass Agnatha (Jawless fish): These are the most primitive fish, lacking jaws and paired fins. They have existed for over 500 million years. Examples include lampreys and hagfish.

2.1.2. Class Chondrichthyes (Cartilaginous fish): These fish have skeletons made of cartilage instead of bone. They have jaws and paired fins. Examples include sharks, skates, and rays.

2.1.3. Superclass Osteichthyes (Bony fish): This is the largest and most diverse group of fish. They have skeletons made of bone and have jaws and paired fins. Examples include tuna, salmon, goldfish, and many more.

• General considerations

 • Quality management principles

 • Objectives of 9001:2008

 1. Scope

 2. Normative reference

 3. Terms and definitions

 • Main clauses and sections of ISO 9001:2008

 4. Quality management system

 5. Management responsibility

 6. Resource management

 7. Product realization

 8. Measurement, analysis and Improvement

Hausman (1920a) worked on the microscopic structure of hair from the commercial fur. The hair samples of 27 different species of animals were used for the study. He described in detail the structural features of guard hair. He classified medulla of hair into 4 different types as discontinuous, continuous, fragmental and interrupted on the basis of composition of medullar cells. He also classified the cuticular scale pattern into two types as imbricate interrupted and imbricate coronal. He mentioned in detail the methods for the preparation of slides for the study of microscopic structures of hair. He also studied the cortex and pigment granules.

1. Introduction:- Understanding Good Laboratory Practices (GLP)
1.1 Definition and Core Principles of GLP
The Organisation for Economic Co-operation and Development (OECD) defines Good Laboratory Practice as “a managerial quality control system that governs the organizational process and the conditions under which non-clinical health and thus, environmental safety studies are well planned, executed/ performed, vigilantly monitored, properly recorded, reported, and finally archived”. This definition highlights that Good Laboratory Practices (GLP) is a comprehensive framework focused on ensuring the quality and integrity of the entire study lifecycle, rather than dictating the scientific validity of the research itself. The primary goal of GLP is to ensure process integrity and data traceability, which are essential for the regulatory acceptance of safety data for a variety of products. These products include pharmaceuticals, pesticides, cosmetics, veterinary drugs, food additives, feed additives, and industrial chemicals, all of which undergo non-clinical testing to assess their potential health and environmental risks. It is important to note that GLP principles specifically apply to these non-clinical studies and exclude research involving human subjects. The OECD Principles of GLP are internationally recognized and serve as the basis for GLP regulations in numerous countries worldwide. These principles outline the responsibilities of key personnel involved in non-clinical studies, including test facility management, the study director, study personnel, and quality assurance personnel. Furthermore, they establish minimum standards concerning the suitability of facilities and equipment to perform studies, as well as the necessity for comprehensive Standard Operating Procedures (SOPs) to guide all aspects of laboratory operations. The core principles of GLP encompass a wide range of elements critical to ensuring the quality and integrity of laboratory studies. These OECD principles of Good Laboratory Practices (GLP) include: the proper organization team and personnel staff members within the testing facility, the well establishment of a robust quality assurance programme, the better suitability and proper maintenance of various necessary facilities, the efficient management of apparatus, materials, and reagents, the characterization and control of test systems, the handling of test and reference items, the development and adherence to Standard Operating

Proper restraint and handling of laboratory and exotic pets reduces stress for both animal and examiner. Handling-related stress introduces a variable into experiments, so its minimizing is essential. Improper handling can lead to significant injuries for both animals and humans. Laboratory Animals General Care •     A healthy and regularly behaving animal is in a state of well-being. Maintaining the state of wellbeing requires effective health management and appropriate exercise. •     New animals on arrival should be quarantined, carefully examined, and placed in clean cages. Shipping containers should not enter the main facility and must be discarded. Proper identification and documentation of each animal, including details like origin and condition, is essential. Any sick animals should be euthanized immediately. •     Animals should be housed in separate rooms

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

• General considerations

 • Scope

 • Microscopic examination techniques

 • Smear

 • Vital staining

• Routine histopathological techniques of sectioning

 • Collection of tissues

 • Fixation

 • Washing

 • Dehydration

 • Clearing

 • Impregnation

 • Casting of blocks

 • Trimming

 • Section cutting

• General considerations

 • Tuberculosis

 • Johne’s disease

 • Brucellosis

 • Infectious bovine rhinotracheitis

 • Campylobacteriosis

 • Tichomonosis