eChapters

Precision feeding aims to supply animals with appropriate nutrients according to their changing physiological state, growth, production and environment. Conventional feeding plans are often static and may lead to waste, increased costs and environmental losses. This chapter introduces an AI-enabled framework integrating animal data, feed composition, growth characteristics and external factors. Regression, ensemble learning and other machine-learning models predict feed intake and weight gain, while linear programming and metaheuristic optimization generate efficient allocation plans. A decision-support system provides recommendations in real time. The approach can improve feed conversion, nutrient utilization, profitability and sustainability while reducing overfeeding, resource wastage and the environmental impact of livestock production.

Animal behaviour provides valuable information about health, welfare, reproduction, environmental adaptation and social interaction. Traditional observation is labour-intensive, time-consuming and vulnerable to observer bias. This chapter explains how machine learning automates behavioural analysis using video, sensor and bioacoustic data. It covers preprocessing, feature extraction, classification, clustering and recurrent neural networks for temporal analysis. Computer-vision techniques—including object detection, tracking and pose estimation—enable continuous and non-invasive monitoring. Case studies demonstrate applications in livestock management, wildlife conservation and laboratory research. The chapter also examines data labelling, model generalization, ethics, explainable AI, real-time processing and the integration of multiple behavioural data sources

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.

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.

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.

Animal husbandry faces increasing pressure to produce safe milk, meat and eggs while improving animal welfare and reducing environmental impacts. Traditional observation-based management is increasingly inadequate for large and complex livestock systems. This chapter introduces digital transformation through Precision Livestock Farming, which combines IoT devices, smart sensors, wearables, computer vision, artificial intelligence, machine learning and digital twins. Continuous animal-centred data enables timely decisions concerning health, reproduction, feeding, behaviour and welfare. The chapter explains how digital technologies can support early disease detection, efficient resource use and improved productivity across commercial and smallholder farms, creating more profitable, sustainable and welfare-conscious livestock systems.

Basic knowledge of statistics is important to understand animal breeding analysis. In this chapter, the hypothesis testing, principles of experimental design, types of data and probability distribution are described with examples using appropriate software.

Correlations and regression are important components of animal breeding analysis. The genetic correlation is an important genetic parameter that is useful in the planning of breeding programs involving multiple traits. In this chapter, the basic concepts of correlation and regression including method of estimation, assumptions and test of significance are presented. An example using SPSS software is also given at the end for better understanding.

Milk yield is one of the most important economic traits and modelling the lactation curve is important with respect to the unique shape with ascending, persistent and descending phases. The non-linear relationship of daily milk yield with days in milk has led to extensive research in modelling the lactation curve. The nature of the lactation curve, different models, identifying the best model of fit, factors affecting the curve parameters and orthogonal polynomials to model the curve are discussed in this chapter.

Participatory curriculum development (PCD) in extension education is an emerging approach to help and improve the quality of education in farm universities. The main steps of the PCD approach adopted here have high practical utility for the design of formal course contents for the Undergraduate and Postgraduate courses in Agricultural Extension. A great deal of thinking is required while deciding on how much participation will be required from which stakeholders, at what level(s).

As stated before, a careful assessment of the existing curriculum is essential before any modification is undertaken and must take into account the country’s education policy, political, economic, demographic, cultural, social and technological environment. Problems of farmers to be solved, needs to be satisfied, and priorities to be identified and addressed and then only the recommendations made for action.

• Content approach

We can identify several different ‘traditional’ approaches to curriculum development. The most common approach until recently is the ‘content’ approach. This is where the curriculum is basically a list of knowledge – things that the agricultural graduating learners need to know. Usually this list is made by a curriculum development committee or group of experts identified at the national level.

In the present system of curriculum changes, participation of all the stakeholders are not actively involved in developing curriculum over the years.

The motive behind conducting the workshop on Capacity Need Assessment and Development is that the existing Extension Advisory Services (EAS) needs new capacities to respond effectively to the emerging challenges in agricultural development.

The results of Focus Group Discussions and Field Level Workshops (Tables presented at the end of toolkit) conducted with different stakeholders mentioned in the methodology revealed that, communication skills, soft skills, leadership development skills, personal and professional development skills followed by entrepreneurship development skills are more important and needed to perform job effectively at field level extension work (Table 1).

The concept of capacity development is defined in various ways. Broadly, it is defined as a process by which skills, and knowledge are built, utilized, retained and nurtured with a view to provide an entity responding to a development challenge. Two components that are vital in any plausible definition of the concept are human skills (the ability to perform a task) and institutions (effectiveness and efficiency of organizations, systems, processes and procedures).

The International Union for the Protection of New Varieties of Plants (UPOV) based in Geneva, Switzerland and established in 1961 is an intergovernmental organization to provide and promote an effective system of plant variety protection, with the aim of encouraging the development of new varieties of plants, for the benefit of society. For plant breeders' rights to be granted, the new variety must meet four criteria under the rules established by UPOV as discussed earlier also.

Things required for cell culture

1. Lab – having negative pressure (HEPA filter)

2. Safety cabinet (Hood/laminar flow)

a) Biosafety level 1 (BSL-1) – Basic level of protection. Agents handled under this cabinet are NOT infectious.

b) BSL-2 – Appropriate for moderate risk agents. Most of the non infectious cell cultures are done under this facility.