eChapters

This crop is gaining importance as a main ingredient in Indian diet due to its medicinal properties. Large areas are under its cultivation and the larger fruited varieties have become very popular. It is one of the prime constituents in ayurvedic preparations
Bark-eating caterpillars - Indarbela quadrinotata, I. tertraonis: Metarbelidae
The larvae of this species make zigzag galleries and feed on the bark under silken ribbon- shaped webs. They are stout and dirty brown in colour.
Fruit- borer : Deudorix Isocrates : Lycaenidae: The larvae of this beautiful butterfly cause considerable damage (up to 42%) to fruits.  They start feeding while the fruit is tender, and growing. During severe infestations, chemical control may be warranted. This can be done by spraying contact insecticides like malathion, or dichlorvos or  fenvalerate or other synthetic pyrethroids.

Appendix 1 : Syllabus (For the Degree of Bachelor Of Veterinary Science And Animal Husbandry (B.V.Sc. & A.H.)

Appendix - 1

Differential Diagnosis of Poisons based on Clinical Signs

Apple codling moth: Cydia (Carpocapsa) pomonella: Tortricidae 

This is an international pest and is now seen in Kashmir, Himachal Pradesh and Tamil Nadu. This is a fruit borer whose moth is silvery white with a dark band on the margin of the forewing and light brownish hind wings with a fringed margin. The moths hold their wings roof-like over the body while at rest.

Introduction

Statistics generally refers to information about an activity or a process that is expressed in quantitate as well as qualitative traits. The quantitative research process is well structured whereas the qualitative one is fairly unstructured. It depends upon how a piece of information have been collected and analyzed. A good researchers need to have both types of skill. This article highlights about the operational procedure, about methods, procedures and techniques that are used in analysis of climate related data.

Parameter vs Statistic

When a data is collected with survey of all the individuals (like in a census) and analysed for certain values, it is called a parameter. But when, we guess the same value with a random sample and interpret for whole population it is called a statistic. Environmental parameters are: mean, variance, correlation, regression, heritability, repeatability etc. Again, there are two types of variables: quantitative and qualitative. Quantitative variables can take any value in a specified range. So, they are of continuous type. But qualitative variables are very few types ( 2 ,3 or 4 types) and they are of discrete type. Body weight is an example of quantitative variable whereas, comb type is a qualitative variable.

Asiatic Elephant (Elephas maximus) is the national heritage animal of India. This particular species now exists in 13 Asian countries including India. As per 2017 census, India is home to an estimated figure of 29,964 elephants that is >60% wild population of the globe. Once distributed across the country, now they are squeezed to 14 states grouped in four regions ie., southern (14,612), north-eastern (10,139), east-central (3,128) and northern regions (2,085). There are 2675 captive elephants across different states under both private and government custody. This largest terrestrial mammal is protected under Seclude I species of Indian Wild Life Protection Act, 1972.

Both central and state Government are implementing short- and long-term programmes for health and well-being of captive as well as free-range elephants. The Central Government has established an ‘Elephant Cell’ at the Wildlife Institute of India (WII), Dehradun to provide technical inputs to the ministry for strengthening the efforts towards conservation of elephants. Add-on, the ‘Project Elephant’ was launched in 1991-92 in the Ministry of Environment, Forests and Climate Change to restore the elephant habitats and reduce their sufferings. Elephant habitats are notified as Elephant Reserves that are basic management units for elephant conservation and managements. A total of 33 Elephant Reserves has been notified in the country. A book titled, ‘Caring for elephants: Managing health and welfare in captivity’ is edited for field officers, veterinary professionals and elephant handlers.

Introduction

Nanotechnology and nanoparticles are increasingly recognized for their potential applications in various aspects of human animal and animal welfare like development of various healthcare or cosmetic products, nano-electronics and techniques for environmental remediation, and many consumer products. Nanoparticles, by definition, are structures that have one dimension in the 1–100 nm range. Nanotechnology involves the application of materials at the nanoscale to new products or processes. Over the past few decades, inorganic nanoparticles, whose structures exhibit significantly novel and improved physical, chemical, and biological properties, phenomena and functionality due to their nanoscale size, have elicited much interest. Nanophasic and nanostructure materials are attracting a great deal of attention because of their potential for achieving specific processes and selectivity, especially in biological and pharmaceutical applications. Nanotechnology has made a new generation industrial revolution by developing product and formulations for medical, agriculture (nano-fertilizers, nano-herbicides, nano-pesticides, recalcitrant contaminants from water, nano-scale carriers, nanosensors), veterinary care, fisheries and aquaculture, detection of nutrient deficiencies, preservation, photocatalysis, nanobarcode, quantum dots etc. It is a rapidly growing industry currently worth billions of U.S. dollars, with many potential benefits to society. Nanotechnology has the environmental applications to protect climate change and environmental pollution as nanocatalyst, light weight nanocomposite materials, nanocoatings, improved renewables and nanosensors.

This fast growing technology is already having a significant commercial impact, which will certainly increase in the future. Because of their widespread application, the commercial nanotechnology industry is predicted to increase significantly to more $3 trillion in few years. While nanotechnologies offer many opportunities for innovation, the use of nanomaterials in food, agriculture and environment has also raised a number of safety, environmental, ethical, policy and regulatory issues.

Veterinarians may encounter disease outbreaks, sudden increases in illness or unexplained changes in herd production. This chapter presents practical approaches to investigating and controlling such outbreaks. Effective investigation requires systematic collection of clinical, epidemiological, management and environmental information. Identification of affected animals, temporal patterns, potential sources and routes of transmission helps establish possible causes. Appropriate diagnostic sampling and laboratory investigations support confirmation. Immediate control measures may include isolation, biosecurity, treatment, vaccination and environmental management depending on the suspected disease. The chapter emphasizes organized outbreak investigation, rapid decision-making and coordinated control measures to limit disease spread and reduce losses.

Indian gypsy moth: Lymantria obfuscata: Lymantridae

Eggs are laid by the bulky adult female moth, which has only rudimentary wings and hence cannot fly.  Eggs are laid under the bark on the tree trunks or on fallen leaves. They feed on many shade trees and other trees also.  Feeding is gregarious and done during night. The trees are almost completely defoliated. Management involves spot application of contact insecticides at the beginning, when the larvae are small and before they disperse to the different leaves.

Spindle bug: Carvalhoia arecae :Miridae

Spindle bug is a serious pest multiplying rapidly with the close of monsoon. The adults are red and black and nymphs are greenish in colour with reddish brown patches. Both these stages colonize the top-most leaf axils at the base of the spindle. Adults and nymphs suck the sap from emerging spindle, tender leaves and other tender parts. For feeding they move out and later return to the axils again. At the site of feeding a longitudinal narrow, decolourised zone develops rapidly on either side of the puncture, along the length of the leaf blade. The decolourised patch gradually turns yellowish, then brownish, become stunted, twisted and malformed in a few days and dries up. In this way the tissues may break away and drop off in the wind leaving shot holes on the leaves. In cases of severe infestation the leaves get shred, stand erect and the palms become stunted.

S. enterica subspecies arizonae (Salmonella arizonae), the cause of an acute septicemic disease in young turkey poults.

Host Range

S. arizonae are reported from a variety of avian, mammalian and reptile species. Among avian in addition to turkeys, it has been reported in chicks, ducklings, psittacines and passerines.

Transmission

Organism is present in the intestinal tract of infected birds. Hence transmission is similar to motile salmonellae. Many workers have reported transmission in turkeys through infected ovaries. Wild birds, reptiles, rats and mice are reported as the common reservoir of the organism. The organism is also transmitted by direct contact and through contaminated feed and water.

Aphids: Aphis gossypii :Aphididae

The aphids congregate on growing points and suck the sap. Honey dew secreted by the aphids can cause sooty mould in severe infestation. Attack leads to yellowish speckling and curling of leaves.
 
Thrips: Caliothrips indicus: Thripidae

Characteristic silvery white specks with black dots which are mostly faecal pellets, develop on leaves. Consequently, the leaves dry up.

Artificial insemination (AI) is a technique in which sperm cells loaded in straws and stored in liquid nitrogen are thawed and manually deposited into the reproductive tract of a female animal at estrus. The recto-vaginal technique is the most commonly used method for artificial insemination in cattle. In order to increase the probability of pregnancy, the animal in estrus should be inseminated at proper time not too early or late.

Introduction
Antimicrobial resistance (AMR) has emerged as a critical global health threat, diminishing the effectiveness of antibiotics against bacterial infections. Conventional strategies to counter AMR—including antibiotic stewardship programs and the development of new drugs—are often slow, expensive, and labor intensive. However, artificial intelligence (AI) and machine learning (ML) present ground breaking alternatives to address this challenge. By leveraging advanced computational techniques, AI can significantly speed up the discovery of novel antibiotics, enabling researchers to identify promising drug candidates more efficiently than traditional methods. Additionally, ML algorithms enhance treatment optimization by analyzing patient data to recommend personalized antibiotic regimens, reducing misuse and resistance risks. AI also strengthens surveillance systems by processing vast datasets— such as genomic sequences and electronic health records—to detect emerging resistance patterns in real time. These capabilities allow for proactive interventions, helping healthcare providers and policymakers stay ahead of AMR outbreaks. Together, AI and ML offer a transformative approach to mitigating AMR, combining speed, precision, and scalability to combat one of the most pressing medical challenges of our time.
AI in Antibiotic Discovery
One of the most promising applications of AI in AMR mitigation is in antibiotic discovery. Traditional drug development is costly and time-consuming, often taking over a decade [1]. Machine learning models can analyse vast datasets of chemical compounds to predict their antibacterial properties. For example, researchers at MIT used deep learning to identify halicin, a novel antibiotic effective against drug-resistant bacteria. AI-driven platforms can also repurpose existing drugs, reducing the time needed for clinical trials [2].
Fig. 1: AI cyclic interface in antibiotic discovery
Predictive Analytics for Resistance Patterns AI-powered predictive analytics enables early detection of antimicrobial resistance (AMR) patterns by processing clinical, genomic, and epidemiological data. Machine learning models analyse electronic health records (EHRs) [3] and bacterial genome sequences to identify emerging resistance threats in real time. These predictive insights allow hospitals to take proactive measures— such as optimizing antibiotic prescriptions—before resistant strains spread. Beyond clinical settings [4], AI models assist public health agencies in forecasting outbreaks and strategically deploying resources. By integrating large-scale datasets, these systems enhance surveillance, enabling faster, datadriven decisions to curb AMR progression. This approach not only improves patient outcomes but also supports global efforts in combating antibiotic resistance through smarter, pre-emptive interventions [5].

Climate change affects livestock through heat stress, water scarcity, unstable feed supplies, altered disease patterns and extreme weather. This chapter examines how artificial intelligence supports climate-smart and sustainable animal husbandry. Machine learning, predictive analytics and IoT-enabled monitoring can optimize feeding, detect disease, assess welfare and improve the use of water, energy and other resources. Intelligent technologies can also assist in reducing greenhouse-gas emissions and developing adaptive production strategies. The chapter evaluates the contribution of AI to productivity, climate resilience and environmental responsibility while addressing problems such as insufficient data, limited infrastructure, implementation costs and ethical concerns. Future integration with sustainable agricultural frameworks is also discussed.

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.

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%.

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.