eChapters

Traits such as growth, lactation test day yield, semen evaluation are longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling these traits, The methodology used, data requirement, assumptions, validity, software available and application of RRM are explained in this chapter.

Growth, test-day milk yield, and semen traits are examples of longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found suitable for modeling such traits. RRM is based on a covariance function which could be defined as “a continuous function to give the variance and covariance of traits measured at different points on a trajectory” (van der Werf, 2001). Kirkpatrick et al. (1990) showed that variance components for longitudinal data can be modelled through covariance functions. Random regression models (RRM) and the resulting covariance function have been recognized as ideally suited for the analysis of longitudinal data (Hill, 1998; Meyer, 1998).

Maintaining the required type of information in the form of records is primary for any type of animal breeding analysis and this chapter explains the importance of records and ways to maintain them in the form of registers or computer databases. The formats for different types of registers to be maintained are also given as a template in appendix. Any breeding plan is formulated to improve the economic traits depending on the utility of species reared and the chapter lists important economic traits of livestock and poultry with definitions and formulae, where ever required.

Basic record maintenance is a must for any animal breeding program. Every farm or breeding program is expected to maintain records in the form of registers or computerized databases. The goal of animal breeding is to select mates with the objective to improve economically important traits. In this chapter, the most common economic traits in different species of livestock are described. Even though the list is not exhaustive the basic economic traits that have been worked for improvement are explained. Breeders also explore new composite traits which are a combination of these base traits.

Meta-analyses has been used extensively to combine the results of existing studies and obtain powerful estimates of a parameter. In animal breeding, attempts have been made to obtain meta-analysis estimates for genetic parameters. This tool is more pertinent to animal breeding on account of heterogeneity in terms of location, sample size and other factors in the estimation of genetic parameters. This chapter explains the background, methodology, precautions and implications of meta-analysis with respect to genetic parameters.

A meta-analysis is a powerful tool of statistics that could combine several estimates from different studies for a parameter and the resulting weighted estimate could be used as a representative value for the particular parameter. However, not all the studies are similar with respect to location, sample size, and methodology used and thus various precautions are to be taken right from the selection of studies to incorporating of between-study heterogeneity in the models. In animal breeding, met-analysis is very much useful for obtaining meaningful estimates of genetic parameters and this chapter explains the methodology of meta-analysis in animal breeding.

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained.

Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations.

Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

The aim of the breeder is to improve on various economic traits through selection. However, due to the lesser number of parents used, over a period of time, there creeps in the effect of inbreeding and loss of genetic variability which in turn causes depression and loss of fitness. This is more prominent in finite endangered populations and evaluation for genetic variability also forms an integral part of the animal breeding analysis.

Genetic variability is an integral part of domestic animal populations as fitness is directly related to the variability.

The large volume, type and nature of animal breeding data and the complex nature of analytical requirement warrants additional tools for handling, storage, retrieval and solving complex equations. Accuracy, speed, computational requirement, applicability, etc., has resulted in the development of numerous software exclusive for animal breeding data analysis. The database management system is already explained in the chapter on ‘Nature of animal breeding data and database management’. This chapter gives a note on the commonly used animal breeding software.

Introduction

Resources must be managed effectively to achieve maximum sustainable agricultural production from limited natural resources. Many of the resources that agriculture depends on (such as water and land) are finite and need to be well managed for long term sustainability. In most cases, this means avoiding adverse effects on the society and environment. With the development of agricultural sectors, the utilization patterns of natural resources in agriculture are important concern for policy makers in present climate change scenario. Unlike other production activities, agricultural activities also utilize factor resources to produce several marketable products or services. The way the natural resources are utilized in agriculture and its concern for society and environment, plays a vital role in economic viability and ecological sustainability of agriculture.

There are several conventional methodologies to estimate the level of profit and efficiency of production system at enterprise level. Only marketable factors are considered in conventional methods keeping the non-marketable component of production system away. Non-marketable component of a production system includes various social and environmental dimensions of it. Nammalwar (1997) has explained the concept of environmentally sound and sustainable enterprises, which include the physical resources, ecological resources, human use value, quality of life values with either way impact of agriculture and environment. In recent years, non-marketable dimensions of agricultural activities have been given importance because of its concern for the environment and society. This chapter provides a methodology that incorporates non-marketable component of agricultural enterprise besides its marketable component. The Resource Cost Ratio (RCR) approach is a modified methodology developed by Debnath et al. (2010) from Domestic Resource Cost Ratio (DRCR) that Morris (1990) has applied at macro-economic level to determine the comparative advantage between countries.

Precision agriculture uses spatially and temporally variable information to improve crop management, resource efficiency and agricultural productivity. This chapter examines the integration of artificial intelligence throughout the precision agriculture workflow. It introduces variable-rate technologies, georeferenced data collection and decision-support systems, followed by machine learning, deep learning, computer vision, reinforcement learning, fuzzy logic and expert systems. Applications include crop yield prediction, plant disease monitoring, soil property estimation, irrigation planning and autonomous agricultural operations. Remote sensing, satellite imagery, IoT sensor networks and edge computing are considered for real-time decision-making. The chapter also discusses agricultural robots and autonomous machinery, practical field applications, data limitations, hardware costs, connectivity and model interpretability.

Artificial Intelligence (AI) is becoming an important tool for addressing complex challenges in plant science and agriculture. This chapter introduces the fundamental concepts, methodologies and emerging paradigms of AI applied to plant research. It discusses machine learning, deep learning, convolutional and recurrent neural networks, and transformer architectures, with applications in plant phenotyping, disease diagnosis, genomics and precision agriculture. Different data modalities, including imaging, LiDAR, remote sensing, genomic sequences and metabolomics, are examined. The chapter also considers transfer learning, data augmentation, explainable AI, graph neural networks, federated learning and multimodal AI, together with challenges, ethical issues and future research directions

Appendix A: Diagrammatic Presentation of Processes Involved in Obtaining an Indian Patent

1.1    General Introduction, History, Ownership of Tangible and Intellectual Property
    
1.2    Patentable Subject Matter
    
1.3    Basic Requirements of Patentability
    
1.4    Novelty, Public Domain, Obviousness and Non Obviousness
    
1.5    Inventive Step (Non-Obviousness) and Industrial Application

3.1    Competitive and Collaborative Research
    
3.2    The Strategy to Protecting Plants : Plant Variety Protection Act
    
3.3    IPR Issues in Biotechnological Patents Haldi, Neem and Basmati Rice
    
3.4    Patent Litigation

The Madrid System for the International Registration of Marks is governed by two treaties. The first one is he Madrid Agreement, which was concluded in 1891 and revised at Brussels (1900), Washington (1911), The Hague (1925), London (1934), Nice (1957) and Stockholm (1967), and amended in 1979. The other one is the Protocol related to this agreement which was concluded in 1989. The aims of this protocol are to make the Madrid system more flexible and more compatible with the domestic legislation of certain countries or intergovernmental organizations that had not been able to accede to the Agreement. 

6.1    Copyrights
    
6.2    Trademarks
    
6.3    Geographical Indications
    
6.4    Trade Secrets

4.1    Provisional and Complete Specifications
    
4.2    Publication
    
4.3    Request of Examination
    
4.4    PCT Application: Gateway to Foreign Patents

5.1    TRIPS
    
5.2    WTO
    
5.3    WIPO
    
5.4    Paris Convention
    
5.5    Berne Convention
    
5.6    Budapest Treaty
    
5.7    Madrid Agreement Marks (MAM)
    
5.8    The Patent Law Treaty (PLT)

2.1    Specification Drafting
    
2.2    Methods and Precautions in Drafting Patent Specification
    
2.3    Disclosure Requirements
    
2.4    Patent Claims
    
2.5    Filing of A Patent Application

Two-dimensional nanomaterials have expanded the possibilities for designing advanced green composites with tailored multifunctional properties. This chapter focuses on MXenes and nanosilicates, including laponite, montmorillonite, and halloysite, as important alternatives and complements to graphene-based nanomaterials. Their integration into green composite matrices can influence mechanical strength, thermal stability, barrier characteristics, surface functionality, and other performance parameters. The chapter examines the development of advanced green composites based on these nanomaterials and considers their potential for sustainable material design. Particular attention is given to the relationship between nanoscale structure, matrix interaction, and composite performance, highlighting opportunities for multifunctional and environmentally responsible material systems.