eChapters

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.

Background

Sheep and goats are the poor man’s cow because of the lower capital investment, and production costs. Also supplies milk quantities that are suitable for immediate household consumption thereby reducing problems of milk storage and marketing. Small ruminants, goats and sheep, mainly localized in the arid, semi-arid and mountainous areas of the country, have multi-faceted utility in terms of wool, meat, milk, skins, (mo)hair/pelts, manure and have also acquired socio-cultural niches in many communities (Kosgey and Okeyo, 2007). Goat and sheep milk has many advantages over other livestock species’ milk being easy in digestion, non-allergic, naturally homogenized, improves immunity as it has antifungal and anti-bacterial properties. However, rearing small ruminants for meat is more popular among farmers as price of chevon and mutton are very high, continuously increasing over the years and sale of male for meat is easy, steady and accessible at farmer’s place or local places unlike milk, skin and fibre. Thus, small ruminants (SR) occupy a promising agrarian and economic niche in low to medium input production systems, not suitable for growing crops and dairy farming (Devendra and Chantalakhana, 2002), mostly driven by small and marginal farmers and landless laborers. Their meat and milk production represents 4.8% and 3.4% of the total meat and milk produced, respectively, in the world.

People all around the world have been conducting genetic association studies for a long time. Still the literature to practically understand the statistical analysis of such data using software packages is scarce. Statistical analysis of molecular data is a vast topic. So we will be undertaking one of the important basic and preliminary topics “Statistical analysis of genetic association studies among unrelated individuals” in this chapter. Overall, we will be discussing population association studies in which qualitative and quantitative data on unrelated individuals for various economic traits is collected and these individuals are also typed at a number of Single Nucleotide Polymorphism (SNP) markers. This excludes family-based association studies, admixture mapping or linkage studies. Various software packages used in this chapter to analyze molecular data of a population are given below in Table 1.

Livestock is an integral component of Indian agriculture which is primarily a mixed crop-livestock farming system and plays an important role in the socio- economic life of India. Livestock contributes significantly to the growth and development of agricultural sector by providing a rich source of high quality products such as milk, meat and eggs and by supporting livelihood of more than two third of rural population. Livestock has a crucial and rapidly increasing role infood security and economic stability worldwide Livestock sector contributes for 4.11% of GDP and 25.6% of total Agriculture GDP. The importance of livestock has been undergoing a steady transformation with the contribution of livestock in total agriculture and allied sector Gross Value Added increasing from 24.32% in 2014-15 to 28.63% in 2018-19. Owing to factors such as population and economic growth, urbanization, improvements in transportation and storage practices,its future importanceformeeting human nutritional requirements of food and fiberand a major contributor to agricultural GDP is evident.

To alleviate the concerns raised by a rapidly growing demand of food security and the need of ensuring sustainability of natural resources, we need to better understandthe ways which wouldspeed up commercialization of livestock production so that it can contribute effectively to the bio-economy. Livestock performance and productivity can be improved by adopting proper diet and feeding practices, improved farm management and disease controlstrategies.

The overall number of animals kept as livestock in India comes to 535.78 million, making it the country with the greatest livestock population. According to the 20th Livestock Census, India has a total bovine population of 302.79 Million. This includes Cattle, Buffalo, Mithun, and Yak. There are a total of 109.85 million buffaloes living in the country. The number of goats in the country currently stands at 148.88, while the total number of sheep in the country is 74.26 Million. Most of the breeds in India have evolved naturally through adaptation to varied agro-ecological conditions and have generally been named after their place of origin or on the basis of prominent morphological characteristics. The number of registered breeds in India has increased over time. Majority of livestock breeds are reared by smallholder farmers are of non-descript type. Indiscriminate crossbreeding is extensively occurs resulting in herds of females that may have very complex ancestries.

Introduction

Due to global population growth, rising wages, and urbanisation, the demand for animal products has been expanding quickly in many areas of the world (FAO, 2018). Meeting the rising demand for animal products, which is expected to reach 8.6 billion by 2030 (FAO, 2018) as the world’s population is projected to increase by 80 million per year (Preisinger, 2012), is one of the biggest difficulties facing modern farm animal industry (Eggen, 2012). Animal products and value-added foods are being consumed as a result of urbanisation and income growth (Hoffmann, 2005; FAO, 2011). By 2050, the world’s population will be close to 10 billion, even as the economic conditions continue to improve the demand for animal products will significantly rise. In order to boost animal production, it will be necessary to acquire a more in-depth understanding of animal biology through genomics and other related f ields of study. Understanding animal physiology remains a significant problem in the context of sustainable agriculture and animal husbandry, particularly for developing animal farming systems that cater to the new and diversified consumer demand. Livestock and poultry productivity has been greatly increased through traditional selection based on genetic merit estimated from phenotypic and pedigree data. Most breeding plans do not take population impacts on genetic diversity into consideration, and selection is geared more towards the best genetic response for the upcoming generation than the best long-term response (Meuwissen, 1997). Breeders have employed genetic analyses to find superior animals for more than 40 years.

Introduction

The expanding number of people around the world necessitates an increase in the amount of food that is yielded. As stated by the Food and Agriculture Organization of the United Nations (FAO), global food production ought to be ramped up by a factor of two in the next few years in to meet the requirements of the growing global population in terms of consumption (FAO, 2006). This is because the global population is expected to continue growing at a rate of approximately one person every two seconds until the year 2050. It is possible that the predicted production disparity could be filled by making improvements in genetics, animal health, and the excellence of animal husbandry.

The production of animals has expanded significantly over the course of the last several decades as a direct result of the application of quantitative genetics in animal breeding. However, due to technological limitations, the use of genetic markers in breeding programmes has only been implemented to a relatively small extent (Deng et al., 2016). The concept of genomic prediction, which was first introduced approximately twenty years ago, has completely transformed the planning and execution of animal breeding operations.

Genetic improvement of farm animal species is accelerating in developed countries with adoption of Genomic selection (GS) due to availability of complete reference genome sequence i.e. gene sequences that can be used to compare and study animal genetic traits. GS has been considered most promising for genetic improvement of the complex traits controlled by many genes (Quantitative traits) each with minor effects as using the conventional methods of selection and breeding the rate of genetic gain per generation is very slow with respect to human demand for livestock products. With this method, animals or plants can be selected for selective breeding on the basis of their genetic merit predicted by whole genome molecular markers spanning over the entire genome. Genomic selection may allow the identification of superior individuals for traits such as increased disease resistance which are not currently considered in animal breeding plans because of technical difficulties. The method can predict the genetic merit of young bulls with greater accuracy as compared to convention methods of genetic merit estimation. Genetic gain (ΔG) in animal breeding programs depends upon the intensity of selection (i), accuracy of predictions (r), genetic variance (σ2g) and generation interval (IG): ΔG = i * r * σ2g / IG (Ibañez Escriche & Gonzalez-Recio, 2011).

Genomics is the study of whole genomes of organisms, it uses a combination of DNA sequencing methods, bioinformatics to sequence, assemble, analyse the structure and function of genomes. In another words, it can be defined as it is the study of the structure, function, and inheritance of the genome of an organism. Moreover, genomics focuses on interactions between loci and alleles within the genome and other interactions such as epistasis, pleiotropy and heterosis. Genomics is divided into two basic areas: structural genomics, characterizing the physical nature of whole genomes; and functional genomics, characterizing the transcriptome (the entire range of transcripts produced by a given organism) and the proteome (the entire array of encoded proteins).

Most biological traits are controlled by a large number of genes, which, in addition to their additive effects, may interact with each other, and their expression might be altered based on a variety of environmental effects. Researchers who were among the first to use molecular genetics to the study of cattle began by employing DNA markers to locate genes or sections of the genome affecting features of interest. For instance, identifying a group of genes based on Genome wide association study (GWAS) provides evidence that a causal relationship between a phenotype and a single nucleotide polymorphisms (SNPs) may exist. These findings of GWAS, however, do not improve our understanding of complex traits in which many pathways may be involved. The growing use of genotyping technologies has generated a large quantity of genomic information on the dairy population. Since the draft sequence of the bovine genome was published in 2009 (Zimin et al., 2009), whole-genome sequence data of cattle have also become available.

The systematic identification, evaluation, and integration of the body of published work produced by researchers, scholars, and practitioners is achieved through a rigorous and repeatable method called the review of the literature. It acts as a comprehensive resource, incorporating the most recent information, important discoveries, and theoretical breakthroughs linked to a certain field. The literature review is a useful compilation of important discoveries as well as methodological and theoretical contributions within a certain topic that is frequently seen in academic and scholarly journals.

In all academic areas, the review of literature is a crucial component of research. Its main goal is to assess, evaluate, and make links between our findings and previous research on a particular subject of interest. In addition, the literature review acts as a point of reference and offers helpful insights in the following ways:

i. Approaches

ii. Methods

iii. Variables

The study, which was conducted for nature and dynamics of farm ecology in occupational migration in some areas of West Bengal with the stipulated objectives, the following course of action can be considered in future while conduction repeating study.

• Gender dimensions on occupational migration is the future research area.

• Educational issues for families on transit could have been a very interesting area of research.

• Climate refuge; that is migration due to climate change can be an important area of research.

• Application of data analytics could be effective for modelling and simulation on occupational migration.

• The social ecology of migration itself, is a zone of future research.

Summary

It has been found from the study that for the independent variable Family size (x1 ), the maximum is 9.00, and minimum is 2.00 where Coefficient of variation of this variable is 38.124 per cent, which shows that the level of consistency in the distribution is high. For Average family education (x2 ), the maximum is 6.00, and minimum is 1.250 where Coefficient of variation of this variable is 33.074, which shows that the level of consistency in the distribution is high. Average age (x3 ) of the respondents, the maximum is 58.00, and minimum is 12.500 where Coefficient of variation of this variable is 35.341, which shows that the level of consistency in the distribution is high. The study reveals that Average family income (x4 ), the maximum is 10833.00, and minimum is 3666.00 where Coefficient of variation is 30.796, which shows that the level of consistency in the distribution is high and Family land holding (x5 ), the maximum is 5.500, and minimum is 1.00 where Coefficient of variation of this variable is 38.241, which shows that the level of consistency in the distribution Family land of holding is high. In the study maximum and minimum House type (x6 ) of the respondents are 9.00 and 2.00 respectively where Coefficient of variation of this variable is 30.399, which shows that the level of consistency in the distribution is high. In case of material possession (x7 ), the maximum is 13.00, and minimum is 1.00 where Coefficient of variation of this variable is 70.780, which shows that the level of consistency in the distribution is medium. Source of information for migration (x8 ), the maximum is 4.00, and minimum is 1.00 where Coefficient of variation of this variable is 35.679, which shows that the distribution of the variable is high. It has been found from the study that for the independent variable money required for migration (x9 ), the maximum is 5.00, and minimum is 1.00 where Coefficient of variation of this variable is 26.639 per cent, which shows that the level of consistency in the distribution is high.

This chapter deals with the findings of the study along with the discussion based on the analysis. This study tried to represent its findings as per the objectives of the study. At the end of this chapter, interrelation has been made, explanation has been tried to put out down and attempt has been done to reveal the cause behind it. The results and their pertaining discussion are presented according to the specific objectives of the study.

This table 6.1 presents the distribution of 15 independent variables (x1-x15 ) in terms of their Minimum, Maximum, Mean, Variance, Standard deviation, and Coefficient of variation.

The deliberation on the methodology has been made to understand the concept, methods and techniques which are utilized to design the study, collection of information, analysis of data and interpretation of the findings for revelation of truths and formulation of theories. These chapter deals with the method and a procedure used in the study an consist of eight main parts-

A. Locale of research.

B. Pilot study.

C. Sampling Design.

D. Empirical measurement of the variables.

Research setting refers to the detailed information of an area where the study was conducted. The study area generally comprises of a particular geographical area viz. a state, district, block, or gram panchayat area selected according to the convenience of the investigator who possess adequate knowledge regarding the location, communication facility etc. of the locality so that he can easily approach each and every corner of the area for data collection. Besides the investigators must also have the basic knowledge about the socio demographic background of the people so as to have an easy understanding of their knowledge, attitude and behavior. This chapter is intended to present a brief description of the area in terms of social, economic, and agricultural aspects where the present study has been conducted.