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Pork production in India is limited, representing only 9% of the country’s animal protein sources. Production is concentrated mainly in the northeastern states of the country and consists primarily of backyard and informal sector producers. The contribution of pork products in terms of value works out to 0.80% of total livestock products and 4.32% of the meat and meat products. The contribution of pigs to Indian exports is very poor.

The chromosomes of the domestic pig (Sus scrofa domesticus) are known to be prone to reciprocal chromosome translocations and other balanced chromosome rearrangements with concomitant fertility impairment of carriers. The domestic pig has diploid chromosome (2n) number 38. The domestic pig karyotype shows a tendency for balanced structural chromosomal rearrangements, such as reciprocal, Robertsonian, and tandem translocations, as well as inversions. There is extensive polymorphism in the heterochromatin, with G-C-rich heterochromatin located in centromere regions of biarmed chromosomes and less G-C-rich heterochromatin in the centromere regions of one-armed chromosomes.

The diploid chromosome number of the pig is 2n = 38, consisting of 18 autosomal chromosome pairs, which vary in length and morphology (12-bi-armed and 6 one-armed pairs), and two sex chromosomes, XX or XY. Robertsonian translocation is known to occur in the acrocentric chromosomes 13, 14, 15, 16, 17, and 18. In these cases, the chromosomes fuse at the centromeric region, resulting in the production of two derivative chromosomes, a large bi-armed chromosome, and a secondary short chromosome often lost in subsequent cell divisions. Thus, the karyotype of a domestic pig carrying Robertsonian translocation has a distinct 2n = 37 diploid chromosome number, with the noticeable addition of a novel large bi-armed chromosome.

Origin of Fowl

Domestication of poultry seems to have been undertaken in south-east Asia. The chickens were brought to India by 1000 BC, and later on they spread north, westwards and reached Greece by 525 BC. By the beginning of Christian era, the birds were already popular in West Asia and East Europe, and then gradually reached South Africa, Australia, Japan, USSR and USA.

Red Jungle fowls are the ancestors of the present-day poultry breeds. There are four known species of wild fowl and they belong to the same genus “Gallus” meaning a cock. The four species are as follows:

The Red Jungle fowl is supposed to be the main contributor for the development of modern-day poultry and is widely distributed throughout Burma, China, India, Philippines, Sumatra and Thailand. The plumage (colour of the feather) of females resembles that of Brown Leghorn, the males have orange red feathers in hackle, wing bow and saddle regions, while the breast is black. Eggs are buff (off-white) in colour. The legs are slate (dark bluish grey) coloured. The comb is all red.

In India, goats are known as “poor man's cow” because they are an important source of income for poor and landless people. They can be reared on cheaper resources easily than other livestock.

Goats come under the genus Capra have evolved from five wild species, including Capra hircus (which includes Bezoar), Capra ibex, Capra caucasica, Capra pyrenaica, and Capra falconeri. This evolutionary pattern contributes to their phenotypic diversity and adaptability to various environments. The chromosome profile of goats (specifically the Capra hircus species) reveals some interesting features which are as follows:

Chromosome Number
The modal chromosome number for goats is 60.
These chromosomes can be categorized into different types based on their centromere position:
Autosomes: These are either acrocentric or telocentric.
X Chromosome: It is acrocentric.
Y Chromosome: It is submetacentric.

Identification

Quinacrine-mustard staining and fluorescence microscopy allow us to identify 28 out of 30 chromosome pairs in goats.

According to the karyotyping the domestic sheep (Ovis aries) has 54 chromosomes. The following is a brief summary of their chromosome composition:

Autosomes: There are one pair of sex chromosomes, 52 autosomes and three pairs of these autosomes are submetacentric (having a centromere slightly off centre), while the rest are acrocentric (with the centromere near one end).

Sex Chromosomes: The X chromosome is acrocentric and the Y chromosome varies from submetacentric to metacentric.

Important features

• According to the Food and Agriculture Organization Corporate Statistical (FAOSTAT) Database (2019) and database of the United Nations Food and Agriculture Organization, the top three countries by the number of heads of sheep were: China (163.48 million heads), India (74.26 million) and Australia (65.75 million). • India has second largest sheep population in the World.

• Sheep milk is an important product in the Near East and North Africa (9% of total milk production) and sub-Saharan Africa (4%).

• The countries with the most dairy sheep are China, Sudan, Turkey and Algeria (FAO).

• Major sheep milk producers are China, Turkey and Greece.

• More than half of the world’s sheep population is found in developing countries. Sheep are more prevalent than goats in cold climatic conditions. The major produce from Sheep production are milk, meat, skin, fibre and manure. However, most of the small-scale producers in developing countries raise sheep for meat or sale.

The buffalo belonged to the Bovidae family, Bovinae subfamily and the scientific name is Bubalus bubalis. This is descended from the Bubalus arnee or wild Indian buffalo, and is widely dispersed throughout Southern Asia. The world population of buffaloes is approximately 204 million head: more than 98% are in Asia; 0.8%are in Africa, particularly in Egypt; 0.9% are in South America; and 0.2% are in Europe (FAO, 2024).

The countries with the largest numbers of dairy buffaloes are India, Pakistan, China, Nepal and Egypt. In Pakistan and Nepal there are more dairy buffaloes than dairy cows. Water buffaloes are the main source of milk in South Asia. India is the largest water buffalo milk producer compared to Pakistan, where buffaloes produce more milk than cattle.

 Scientific classification
 Genus: Bubalus 
 Species: bubalis
 Wild water buffalo: Bubalus arnee
 Domestic water buffalo: Bubalus bubalis
 Lowland anoa: Bubalus depressicornis
 Mountain anoa: Bubalus quarlesi
 Tamaraw: Bubalus mindorensis
 Cebu tamaraw: Bubalus cebuensis

Important features

• Cattle produce 81% of world milk production, followed by buffaloes with 15 percent, goats with 2 percent and sheep with 1 percent; camels provide 0.5 percent. The remaining share is produced by other dairy species such as equines and yaks.

• About one-third of milk production in developing countries comes from buffaloes, goats, camels and sheep. In developed countries, almost all milk is produced by cattle.

• Milk from dairy species other than cattle represents 40 percent of milk production in Asia, 23 percent in Africa, 3 percent in Europe and 0.5 percent in the Americas; it is almost non-existent in Oceania (FAO)

Taxonomy of Cattle

Cattle belong to the kingdom Animalia, phylum Chordata, class Mammalia, order Artiodactyla, family Bovidae, genus Bos, and species taurus or indicus. Cattle were originally identified by Carolus Linnaeus as three separate species: Bos taurus, Bos indicus, and Bos primigenius. Bovines, which include cattle, bison, African buffalo, water buffalos, and certain antelopes, belong to the subfamily Bovinae.

The term “artificial intelligence” (AI) refers to a wide variety of theories and technologies that are used to the resolution of issues characterized by a high level of logical or algorithmic complexity. It cuts across a wide range of f ields, such as mechanical modeling, software engineering, data science, and statistics. Many artificial intelligence (AI) technologies, which first appeared in the 1950s, have lately been refined or extended thanks to advances in computer performance. Recent advancements have been made possible by the creation of interfaces between artificial intelligence and other fields of study, such as biomedicine, and by the collection of enormous amounts of data from a variety of fields, notably those related with the healthcare industry. First, Artificial intelligence helps with situational awareness, which is relevant in animal health and animal husbandry because it allows for the detection of patterns like repeated sequences of observations, forms like a protein, and signals like increased mortality especially in comparison to a baseline at various scales. A third option is computer-based decision making, or more practically, human decision support via knowledge - based systems, diagnostic support, and resource allocation, for instance.

Biostatistics is a branch of statistics that focuses on the analysis and interpretation of data from biological and health-related research studies. It involves the application of statistical methods to design, collect, analyze, and interpret data from studies involving living organisms like: animals, birds including human beings. Biostatistics plays a crucial role in public health, medicine, epidemiology, genetics, and other related fields.

With the increasing availability of data and advancements in biostatistics, breeders can expect further improvements in breeding practices that will results in better outcomes for both livestock farmers and the animals they breed. Animal breeding is a crucial aspect of agriculture and food production, as it helps to improve the quality and quantity of animal products as well ensuring food security to the masses.

Introduction

Estimated breeding values (EBVs), often estimated for sires, are used in traditional animal breeding (TAB) programmes to make selections based on an animal’s performance and that of its relatives. Using, typically, the best linear unbiased projections, EBVs are calculated using attributes measured in own performance, pedigree, sib, and/or progeny evaluation schemes in specialised testing stations and/or selected farm conditions (BLUP; Henderson, 1975). One of the main time-consuming challenges for phenotypic evaluation to enable a meaningful estimation of breeding values is generation intervals of 4 to 5 years in beef cattle, more than 5 years in dairy cattle, and 2 to 2.5 years in pig breeding schemes (Schefers and Weigel, 2012). Modern breeding techniques like marker-assisted selection (MAS) and, more recently, genomic selection (GS) are based on the use of genetic DNA markers to aid in selection and breeding (Williams, 2005). The fundamental reason why MAS, that only further uses a few markers as a selection tool, hasn’t made as much progress as was initially anticipated is that finding trustworthy markers is challenging, particularly when dealing with complex traits. Only relatively few consistent markers could be identified, despite successful identification of many quantitative trait loci (QTL; Hu and Reecy, 2007)

Livestock breeding for genetic improvement faces many challenges including feed conversion efficiency, fertility and adaptation to changing climate. These improvements have to be realized while maintaining or improving the nutritional properties of meat, milk and other animal products while giving emphasis to animal health and wellbeing also. Environmental stresses induced by climate change affect livestock productivity, reproductive efficiency and health, resulting in severe economic losses. Under the current climate change scenario, the viability of producing animals for human consumption is in jeopardy. In order to lessen the negative effects of heat stress in today’s world, it is urgently necessary to develop methods that are centered on breeding animals with increased thermal tolerance and climate resilience. The breeding of animals having genetic potential to sustain under changing environment conditions can be achieved through the various molecular genetics tools.

Livestock is one of the fastest growing agricultural subsectors in developing countries. In India has a vast diversity of animal genetic resources. Among them, sheep is an important ‘five star’ livestock species contributing significantly to rural economy through production of mutton, wool, manure, skin and milk etc. Sheep make a valuable contribution to the livelihood of the economically weaker sections of the society. The total Livestock population is 535.78 million and total sheep population is 74.26 million in in the country showing an increase of 4.6% and 14.1%, respectively over Livestock Census-2012. And it’s ranking 3rd in the world after China and Australia. Rajasthan is the fourth largest sheep rearing state in India with 7.9 million population (Livestock Census, 2019). Malpura sheep is one amongst the heaviest sheep breed of India, widely distributed in the semi-arid region of Rajasthan, mostly Tonk, Jaipur and Sawai Madhopur districts. Malpura sheep is reared and improved for mutton production since year 1975 at CSWRI, Avikanagar. The objective since then has been to improve the growth characteristics of this sheep breed for producing more carcass yield.The sheep is known for its adaptability to the harsh environment and potential for high meat production.Coat colour of Malpura sheep is white with coarse wool. The face is light brown with short, blunt ears. Animals have square compact body and long legs. Farmers also have liking for the long tail of animals for aesthetic purpose. The farmers like Malpura sheep for its properties like good mothering ability and produce sufficient milk to sustain and accelerate the growth of their neonates (Mishra et al., 2005), higher weight gain, number of lambs weaned, wool quality, walking capacity and the ease of management.

Introduction

Wool which obtained primarily from sheep is an essential textile fiber that is used across the world. Whether for clothing, canvas work, carpets, or even upholstery, wool is a vital resource supporting many industries and occupations. This natural fiber has many benefits, such as being biodegradable, easy to clean and even having a natural structure that traps air which is excellent for heat retention. The annual wool production is 2 million tones. China ranks first in terms of global wool production. With a production of more than 333,624 tonnes of wool in 2020, China accounts for approximately 19% of the global wool clip. Australia produces 16% of global wool and is by far the largest wool exporter in the world. There has been a constant increase in the demand of wool products worldwide. This has made scientists to try to improve livestock and livestock associated derivatives. The conventional system along with the recent biotechnological interventions will be helpful for enhancing wool quality and production.

The global human population is increasing exponentially with time, and it is expected to reach 9.6 billion by 2050 (UN 2013). With rapid population growth, food consumption is predicted to more than double in the next 40 years. Due to limited land resources for cultivation and a stabilising trend in agricultural productivity, the role of livestock sector is increasing significantly to meet the rising food demand. Meanwhile, global livestock production has increased significantly in both number and productivity. Unprecedented urbanisation has a significant impact on food consumption patterns in general, as well as demand for livestock products. Animal diseases are a significant factor in reducing livestock productivity, particularly in developed countries. The limitations of traditional animal breeding hinder further improvement of livestock productivity. The use of biotechnological tools has major applications in improving animal health and production, increasing disease resistance, using assisted reproductive techniques, improving rapid diagnostic techniques, and improving vaccines in livestock systems. These interventions cover advancements in feeding, nutrition, genetics, reproduction, animal health control, and overall improvement of animal husbandry. Since genomic tools and selection have been widely adopted in developed countries to improve both productivity and quality of animal products, the prospects for increasing productivity in developing countries using genomic tools and selection have not been adequately investigated.

Introduction

The key factor of sustainability in the animal sector depends upon genetic improvement in domestic livestock. There are many reproductive and molecular biotechnologies used for the genetic improvement of domestic livestock and they are synergistically coupled with the breeding program in distinct breeding targets. With the help of gene editing techniques (e.g. CRISPR etc.). To improve the specific traits of interest scientists can add, delete or replace the nucleotides (e.g. milk production, meat production, disease resistance, etc.) in less than one generation. Gene editing with the conventional breeding program in livestock is an important factor for genetic improvement for increasing the rate of genetic gain. In the present topic, here, we delved into the most important reproductive and molecular technologies used in livestock genetic improvement, as well as the most effective ways for incorporating gene editing into existing genetic improvement programmes and targeting specific traits for enhancement. The improvement in genetic level is a most powerful part of sustainable animal development because it is permanent and the effects are long-lasting and cumulative. Genetic advancements produced in one generation are passed on to the following, unlike dietary and animal health treatments, which are a continuous process. The many livestock operations that involve chemical and mechanical methods such as dehorning requires time, worker, money, animal safety, etc., but their genetic alternation of the concerned gene for the polled or hornless condition can exclude the requirement of physical dehorning. (Gottardo et al. 2011; Tompson et al. 2017).

India is bestowed with a wide variety of livestock species which forms the backbone of the economy of majority of the farmers. Cattle are one of the important species domesticated by man mainly for their draftability in agricultural operations. Rearing of cattle is an integral part of the Indian agriculture as it is always considered as a subsidiary enterprise of the agriculture farming. The indigenous cattle are well known for their adaptability to the harsh tropical climatic conditions, ability to thrive in the resource poor production systems, better feed efficiency and disease resistance. Barring few dairy breeds, majority of the Indigenous cattle breeds are poor in their genetic potential for milk production. Considering the ever increasing demand from milk , the country has implemented many cattle improvement programmes like Central Herd Registration Scheme (CHRS), Intensive Cattle Development Programme (ICDP), All India Coordinated Research Project (AICRP) on Cattle National Programme for Bovine Breeding and Dairy Development (NPBBD), National Livestock Mission, National Project for Cattle and Buffalo Breeding (NPCBB), Rashtriya Gokul Mission (RGM) etc., for improving the milk productivity of cattle.

Energy is essential for each and every single biological reaction in the body of an individual. Production, reproduction and physiological functions of an animal depend on its body state and utilization of energy from dietary source and its body reserves. Energy reserves are determined primarily by the relative amount of fat accessible as an energy source and are indicative of health condition. Adipose tissue (AT) is a primary reservoir of energy and recognized as an immunologically and endocrinologically active tissue. Cattle have the ability to utilize body reserve to meet energy demand due to negative energy balance during peripartum period, which is crucial for maintaining their health lactation and neonates. There are several methods to measure tissue reserves like body weight, metabolic and hormonal factors, respiratory calorimeter body water by dilution with D2 O, estimation of fat cells diameter, ultrasound techniques etc. Body weight is not a good indicator as it is affected by different factors such as; age, parity, gestation, body frame, gastrointestinal contents weight of vital organs and shows a greater variation in measuring fat stores. Other methods are inapplicable in field conditions as they are costly, time consuming and need laboratory facilities and skilled technicians. However Body Condition Score (BCS) is an easier and the most practical method, which is an assessment of thickness of fat cover, prominence of bone of tail and head region.

Introduction

Raising milk and meat production are long-standing global issues. Researchers are learning that present breeding efforts won't be sufficient to meet anticipated future food demands given the current climate scenario. The animals that influence the overall yield of the field must be screened as soon as possible. In order to build more successful modern breeding programmes, high-throughput phenotyping is currently being included into conventional breeding operations. Despite the public accessibility of their genetic information online, there is still a lack of phenotypic data on the genomes of various animal breeds because environmental factors make it more difficult to define phenotypes and increase the likelihood of measurement error (Rahaman et al., 2015). Genomic selection (GS) is a strategy that predicts the genomic estimated breeding values of lines in a breeding population using information from genome-wide markers (Meuwissen et al., 2001). When compared to other conventional methods like marker-assisted selection (MAS), genomic selection has some inherent advantages. These include boosting genetic gain by reducing breeding cycles and acquiring minor effect loci based on markers dispersed throughout the entire target genome. Reliable phenotypes and genotyping are both necessary for the training of accurate prediction models for genomic selection. Phenotyping is regarded as a significant factor restricting genetic improvements in animal breeding due to high labour and time expenditures. Traditional approaches of phenotyping are prohibitively expensive, time-consuming, and slow.

Introduction

The sex of the animal is one of the most easily observable attributes of a livestock population. The importance of the sex ratio lies in the fact that it sets the future rates of fertility, mortality, and migration. In livestock, male animals are required in fewer numbers compared to females for breeding. So, for profitable management of a farm, the sex ratio has been fixed for each species. But because of the natural 50:50 chance of both types of sperms to fertilize an ovum, this preferred sex ratio is never achieved. In order to reach this economical ratio, additional efforts must be done in the form of culling. Moreover, in monotocous species with nearly one year gestation period, a long wait is sometimes required for getting the offspring of particular sex.

In dairy farming, the number of female offspring born is always an asset to the farm. For the species reared for meat, the males are often avoided due to the intense odor of their meat and handling difficulty while rearing. But when there is shortage of good quality breeding bulls, the sorted sperms will be helpful for producing the elite bulls for breeding. If we can produce only the animal of the desired gender, we can eliminate split-sex feeding, reduce problems related to animal welfare and castration, improve production efficiencies. Additionally, the costs of the progeny testing programme and embryo transfer are reduced as a result, and the value of genetic markers is increased. Apart from the livestock species, sex pre-selection is also important in breeding of companion animals, zoo animals and endangered species.

Artificial Intelligence (AI) is a discipline of computer science that now days affecting evary aspect of day to day life. It has the potential to greatly improve animal breeding programs in India by providing more accurate and efficient selection methods. Some of the avenues and possibilities of using AI in animal breeding in India includes Predictive analytics: AI can be used to develop optimization models that take into account a variety of factors, such as genetics, phenotypic traits, and environmental conditions, to identify the best breeding strategies. AI can be used to analyze large amounts of data on animal genetics, phenotypic traits, and environmental factors to predict the likelihood of an animal producing offspring with desired traits. This can help breeders make more informed decisions about which animals to be selected for breeding. AI algorithms can be trained to accurately identify diseases in animals by analyzing imaging data, such as X-rays and MRI scans, as well as blood and tissue samples which in turn facilitate veterinarians to make more accurate diagnoses, improving treatment outcomes. AI can also improved feed efficiency by analyzing the feed consumption and nutrient utilization data to identify patterns and trends that can be further utilized to improve feed efficiency, reduced feed costs and increased production efficiency.

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.