eChapters

Large Language Models have fundamentally changed natural language processing and expanded the role of artificial intelligence across software engineering, healthcare, education, business and scientific research. This concluding chapter synthesizes the major technological, operational and societal themes addressed throughout the book and develops a roadmap for next-generation AI models. It considers the continuing evolution of model architectures, training methodologies, multimodal capabilities, real-time inference and specialized applications. At the same time, future AI development must address privacy, fairness, transparency, accountability, security, computational cost and environmental sustainability. The chapter emphasizes that LLM development cannot be viewed solely as a race toward larger models; future systems must also become more efficient, adaptable, interpretable and responsible. Emerging directions include self-improving systems, real-time reasoning, multimodal intelligence, domain-specific models and increasingly autonomous AI agents. Lifecycle governance, continuous evaluation and human oversight will remain essential as models become more deeply integrated into society. The roadmap therefore calls for collaboration among researchers, developers, industry, policymakers and users to create AI systems that combine technological capability with safety, sustainability, human values and social responsibility.

Artificial intelligence offers practical solutions to agricultural problems associated with population growth, food demand, climate change and limited natural resources. This chapter introduces machine learning, deep learning, computer vision and natural language processing, together with enabling technologies such as IoT, remote sensing and cloud computing. It explains their applications in crop monitoring, disease recognition, yield forecasting, irrigation, soil assessment and livestock management. AI can improve decision-making, resource efficiency, automation and precision farming while reducing reliance on manual labour. The chapter also examines adoption barriers, including high costs, limited digital literacy, data-security concerns and weak rural infrastructure, and considers future developments in autonomous and climate-resilient farming.

AI-enabled livestock technologies can improve productivity, health monitoring, animal welfare and environmental performance, but they also raise significant ethical and governance concerns. This chapter examines data ownership, privacy, algorithmic bias, transparency, accountability, cybersecurity, socioeconomic inequality and animal well-being. Precision livestock systems continuously monitor animals through sensors, computer vision, machine learning and IoT networks, enabling preventive decisions and automated interventions. However, excessive dependence on algorithms may reduce human oversight and treat animals merely as data-producing units. The chapter advocates an interdisciplinary framework for responsible AI that aligns innovation with animal-welfare standards, farmer interests, social values, equitable access, regulatory requirements and long-term environmental sustainability.

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.

AI-driven animal husbandry is advancing through machine learning, computer vision, IoT, big-data analytics, wearable devices and automated equipment. This chapter considers future applications for monitoring animal health, behaviour, nutrition, reproduction and environmental conditions. Predictive systems can support early disease detection, precision feeding, improved breeding and reduced production costs. However, high implementation costs, data privacy, inadequate infrastructure and limited digital knowledge remain significant obstacles, particularly in developing regions. Future research should prioritize affordable, user-friendly and explainable technologies suited to smallholders. Ethical AI, climate adaptation, multimodal data integration, omics analysis and interdisciplinary collaboration among animal scientists, engineers, policymakers and farmers are identified as essential research directions.

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

IoT networks and smart sensors enable continuous livestock monitoring, overcoming the delay and subjectivity associated with manual observation. This chapter reviews sensor types, communication systems, cloud and edge computing, and their applications in animal-health and behaviour tracking. It proposes a framework integrating multimodal sensors with IoT networks and machine-learning models such as CNNs and LSTMs. The system can detect abnormalities, predict health conditions and provide real-time decision support. Practical benefits include earlier intervention, improved welfare, reduced labour and more efficient production. The chapter also addresses implementation problems, costs, connectivity, data security, technical reliability and ethical considerations associated with constant digital monitoring of animals.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Traditional livestock diagnosis is often reactive and may identify disease only after clinical symptoms become visible. This chapter introduces deep-learning methods for automated health monitoring, early disease detection and predictive analytics. Models analyse images, videos, body temperature, heart rate, activity patterns and historical health information. Convolutional neural networks extract visual features and classify diseases, while LSTM networks identify patterns in time-dependent physiological and behavioural data. Multimodal fusion improves the reliability of predictions by integrating different data sources. IoT-connected systems can generate real-time warnings and support timely intervention. The chapter compares deep learning with conventional approaches and evaluates accuracy, recall, false-negative rates and practical deployment.

Cloud computing and Edge AI provide complementary infrastructure for intelligent livestock management. Cloud platforms offer scalable storage, centralized analytics, model training and integration of data from sensors, cameras, automated feeders and farm-information systems. Edge AI processes information close to the farm, reducing latency, bandwidth consumption and response time. This chapter explains their architectures, workflows and applications in dairy, poultry, swine and small-ruminant production. Uses include disease detection, estrus prediction, behavioural monitoring, feeding and environmental control. Hybrid cloud-edge systems balance centralized intelligence with local autonomy. Challenges involving connectivity, interoperability, cybersecurity, cost, model reliability and ethical animal surveillance are also critically examined.

Decision Support Systems convert farm data into recommendations that improve livestock productivity, health and sustainability. This chapter explains data-driven, model-driven and knowledge-based systems and their principal components: data management, analytical models and user interfaces. Technologies such as IoT, artificial intelligence, machine learning and cloud computing support applications in feed optimization, disease prediction, reproductive management, milk production and animal-welfare monitoring. Dairy and poultry examples demonstrate how DSS can reduce operational costs and strengthen farm decisions. Implementation challenges include inadequate data quality, high costs, limited technical expertise and maintenance requirements. Future systems are expected to integrate blockchain, robotics, Edge AI and increasingly autonomous management technologies.

Artificial intelligence can detect subtle changes in animal health before they become apparent through conventional observation. This chapter examines machine learning, deep learning and predictive analytics for livestock disease prediction and diagnosis. Models use physiological signals, diagnostic images, behaviour patterns, environmental conditions and historical records to recognize abnormalities and estimate disease risk. Wearable sensors and IoT devices enable continuous monitoring and early-warning systems, supporting preventive veterinary care and outbreak management. AI-based detection can reduce mortality, treatment costs and production losses while improving decision-making. The chapter also evaluates challenges involving inconsistent data, model interpretability, rural infrastructure, technical accessibility and the practical implementation of diagnostic systems.

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.

1. The unconsolidated mineral or organic material on the immediate surface of the Earth that serves as a natural medium for the growth of land plants. - Soil Science Society of America

ABIOTIC FACTORS: Environmental influences produced other than by living organisms for example, temperature, humidity, pH and other physical and chemical influences. Contrast it with ‘biotic factors’ - meaning effects due to biological components.

ACTIVATION ENERGY: The energy required before the reaction of concern will proceed. Often, heating will supply the needed energy. Enzymes or other catalysts lower the activation energy of a reaction.

AGRICULTURAL WASTE: The characteristics of the agricultural waste materials make them important in respect of their use and economic value. Agricultural wastes can he put to use because (1) Farm development (improvement of soil health, soil fertility, soil physical conditions and plant protection), it is the source of energy and power, (3) it acts as animal feed and fodder, (4) it is the human food

ACID-FORMING FERTILIZER: A term applied to commercial fertilizers which leave an acid residue in the soil. The amount of calcium carbonate required to neutralize the acid residue is referred to its equivalent acidity.

ALLIC: soil family class term used in Oxisols as a calcareous and reaction class category. Soils placed in the allic class are acid and have low CEC with high amounts of KCl-extractable aluminum in a layer at least 30 cm thick within the control section. If used for agriculture, these Oxisols require large inputs of lime to displace the aluminum and raise pH.

ABSORPTION ACTIVE: Movement of ions and water into the plant root as a result of metabolic processes by the root, frequently against an electrochemical potential gradient.

ACID SOILS: Acid soils with pH below 6.5 are widespread. Main limitations of these acid soils are low pH, low CEC, low status of bases, especially Ca, Mg, and K along with toxicity of Al and Mn. Use of acidifying fertilizers aggravates this situation.

AERATION, SOIL: The exchange of air in soil with air from the atmosphere. The air in a well aerated soil is similar to that in the atmosphere; the air in a poorly aerated soil is considerably higher in carbon dioxide and lower in oxygen.

ACCELERATED EROSION: Erosion in excess of natural rates, usually as a result of anthropogenic activities.

AIRBORNE SENSING: Remote sensing from an airplane. (Related words: airphoto, aerial)

ABSORPTION (of light): A process by which light is taken-up by another material. Examples include soot consisting of tiny black particles, which absorb all visible light; and nitrogen dioxide, a pollutant mostly from diesel and gasoline engines, that absorbs blue light resulting in air with a brown tint.

We are living in a world that is in a grim state of environmental disruptions. Callous developmental activities resulting in pollution of all environmental components and gradually culminating in global warming, climate change, and species extinction are responsible for triggering the processes of environmental disruption. Pollution of air, water, and soil is rapidly assuming alarming proportions.

The very processes of socioeconomic development are now posing threat to economic progress as well as to human health. Ominous changes in global climate are posing threat to life on Earth. The irony is that the major cause of environmental disruptions is anthropogenic rather than natural. Human’s materialistic progress and environmental pollution in our contemporary world go hand in hand.

Air or atmosphere constitutes one of the integral components of the environment. Among all the environmental components, the air has the highest fluidity. Therefore, the entry of a foreign substance into the air imparts a quick and severe effect on the health of the organisms. Air in the biosphere has a mixture of various gases in a dynamic proportion which is conducive to maintaining the health and sustenance of all living organisms. An alteration in the composition of the atmosphere beyond a certain limit adversely affects organisms’ health, and might even be of more serious consequences for life.

Air pollution, thus, is defined as the occurrence of undesirable gases (e.g., carbon monoxide, sulfur dioxide, etc.) or particles (e.g., suspended particulate matter) in such concentrations that are deleterious to terrestrial life (vegetation, animals, and human beings) and some physical assets, like buildings, statues, and monuments. Air pollution especially affects the health of human beings living in highly modified (anthropogenic) ecosystems, e.g., cities and industrial areas.

Ours is a Living Planet because ours is a Water Planet. The health of the Living Planet depends on the health of its water resources. Ironically, the health of its water resources is at stake. And so of its life! The waters of the Earth are in the grips of pollution. Much of the water is already polluted and the rest is increasingly becoming polluted. Water pollution, indeed, is one of the worst problems life on Earth is faced with. Water pollution is halting human progress and is phenomenally affecting all walks of human civilization. Water pollution is even putting a question mark on our sustainable future. This chapter presents the causes, magnitude, and consequences of water pollution and also suggests ways and means to prevent and control water pollution and also attempts to manage water resources.

Life on Earth is rooted in soil. Soil is not just a substratum anchoring the plants and giving a base for all terrestrial animals. It is the largest terrestrial ecosystem, and one of the most wonderful ecosystems on Earth. It inhabits most microorganisms. Microbial populations flourishing within the soil have greater biomass than all the human beings, elephants, and whales put together. It is due to the microorganisms that the soil constitutes the most active ecosystem on Earth.

The health of the soil is crucial for the health of all the plants growing on it and for all the animals dependent on it for their inhabitation and nourishment. Soil health is also critical for the well-being of numerous microorganisms, detritivores, and several other organisms. Soil is composed of all essential nutrients/ minerals, air, water, and organisms in a proportion that strikes its ecological balance and maintains its structure and functions revealed through its productive potential.

Soil, like all other ecosystems, is itself a dynamic ecosystem. Its composition keeps changing but natural inbuilt mechanisms help strike a balance and recover 

A sound is a form or energy. Therefore, it has to have an impact on life in one way or the other. Most of the organisms of Kingdom Animalia communicate with each other creating a peculiar sound. Creating sound is inevitable for performing various life activities. Every animal species has the characteristic sound it reflects through its behavior. Whatever all the animals communicate through creating specific sounds is all-natural. Nature, as such is a stupendous repository of a diversity of sounds, all being specific characteristics of life.

Human behavior, however, is highly evolved in terms of creating a variety of sounds. Communication, entertainment, and many behavioral activities are expressed through creating distinctive sounds. There is a difference between natural sounds and the ones human beings create or can create. Humans often generate sounds, not just in a natural manner, but also with various technical aids, that often become unpleasant what we call noise. Creating noise, or noise pollution, seems to be an exclusive characteristic of the human species. Of course, there could be some natural sounds that might occasionally cause havoc to life. Examples are the sounds generated at the time of the volcanic eruption, during an earthquake

Radioactive pollution is largely attributable to radioactivity, a natural property of certain elements, such as radium, uranium, thorium, etc. of spontaneously emitting protons (alpha particles), electrons (beta particles), and gamma rays (short-wave electromagnetic waves) through the disintegration of their atomic nuclei (nuclides).

An alpha particle consists of two protons and two neutrons, which is akin to a helium nucleus. Alpha particles travel at a very slow speed, whereas gamma rays propagate at the speed of light. The alpha particles have less penetration power than the beta and gamma rays. In human tissues, alpha particles can penetrate up to a depth of about 0.005 to 0.008 cm. The gamma rays, on the other hand, are the most penetrating and can easily pass through a human body without striking anything in between. The high penetration power of gamma rays is because they have no charge; therefore, they do not interact with anything as the charged alpha particles would do

Global warming is the hottest issue facing life in our times. The gradual average increase in air temperatures near Earth’s surface over the past one to two centuries is leading to the phenomenon called global warming. It is this global phenomenon that triggers climate change, threatening the entire life on Earth. Earth’s climate has changed over all conceivable geological timescales, but that was all on account of the natural phenomenon. But, human activities are making the phenomenon more pronounced since the beginning of the Industrial Age, now emerging as a dominant triggering factor in influencing the overall climate fabric of the planet.

The forest cover in most tropical countries has been under severe pressure and is diminishing at an alarming speed; consequently, they are experiencing severe biodiversity losses, reverting to a mature state at a slower rate with low resiliency. These tropical countries are aware of the danger causing due to the loss of forests, and large afforestation programmes have been launched by forest departments, private companies, and donor-supported projects. Nevertheless, plantation activities have rarely been able to keep pace with the deforestation rate, not to mention replacing what has been lost during the past. Plantation programme through afforestation activity is not only the matter of the forest department alone but also the combined responsibility of all the foresters. The appeal of forest planting has changed over the years following the broader demand spectrum.

To some extent, the forest plantations fail to create the diversity of plants and wildlife that has been lost. Hitherto, large-scale plantations are still necessary both directly to cater to the needs of industrial wood and help to minimize the pressure on the remaining natural forests. During the past decades, many government programmes have emphasized using 

Seed handling ensures the potential utilization of seed material. Collection and utilization of quality seeds are indispensable in producing quality planting material and better seedling establishment. After selecting the quality seed source, collecting good quality seeds physiologically and genetically is considered the first step in seed handling, followed by processing seeds, testing, storage, and pre-treatment for germination. The main goal of seed handling is to attain an excellent seed survival rate. The chances of seeds getting damaged during any stages of seed handling because of careless or rough approaches are very high.

From the regeneration point of view, the quantity of seed production per tree correspondingly has a vital role to play. If considered virtually, only one successful seed (or two if the species is dioecious) would serve to replace the parent tree(s); hence the production of seeds during the lifetime of a tree is absurd. A full-grown Eucalyptus camaldulensis tree may produce a million or more seeds per year and live further, producing seeds for over a century. Each year’s production could help afforest

Agricultural Economics is an applied discipline that examines the economics of agricultural commodity production, marketing, exchange, and consumption in the economy. It largely focuses on how a society decides to use its limited resources, which have alternative uses, to produce things for both current and future consumption. Due to this shortage, people are forced to choose between options, and economic knowledge is utilised to compare the options and select the best one. For example, a farmer can grow paddy, sugarcane, banana, cotton etc. in his garden land. But he has to choose a combination of crops depending upon the availability of labour and irrigation water.

The emergence of economic problems is caused by two key reasons.. They are: (i) the existence of unlimited human wants and (ii) the scarcity of available resources. The existence of human wants is the starting point of all economic activity in the world. Unless we make efforts, we cannot satisfy wants. Hence, wants, efforts and satisfaction form the circle of economics. Thus, the science of economics focuses around want - effort - satisfaction.

Economics and Reasonability

The four primary economic activities of production, distribution, consumption, and resource management are what we referred to as economic actors or economic agents in Chapter 1. Individuals, small groupings (such as a family or a group of roommates), as well as big businesses like the government or international corporations, can all engage as economic agents. Economics is the study of these actors’ actions and interactions during economic transactions. This chapter examines how people behave as independent economic players. We analyse this subject’s ramifications for economic theory and examine both historical and recent research on it.

Perspectives from History on Economic Behavior

A social science, economics is concerned with people and how we arrange ourselves to meet our wants and improve our well-being. All economic behaviour is ultimately just human behaviour. Even though institutional factors can sometimes appear to gain control (as evidenced by some bureaucracies’ propensity to grow over time), if you look closely at economic outcomes

Goods can be classified as follows

1. Free goods: Goods which are not scarce and available at free of cost are called free goods. Eg: air.
2. Economic goods: Economic goods are scarce and one has to pay a price for it : eg: Rice, pen, cloth etc.
3. Visible and Invisible goods: Things which can be seen and felt can be called as visible goods. Such goods are also called as tangible good. Eg. Rice, bicycle.

   Invisible goods are also called as services or intangible goods. We cannot see or feel those goods. Eg. Copyright of a book, service of a doctor.

4. Consumer goods and capital goods (Producer goods): Consumer goods satisfy our wants directly. Eg. Rice, Pen, etc.

   Goods like machinery, equipments will not satisfy our want directly but used for further production

Agriculture economics is the study of how resources are used, distributed, and utilised in order to create agricultural products. A consistent level of farm surplus is one of the sources of technological and commercial expansion, so agricultural economics has an impact on the economics of development.

In general, it may be said that average salaries are low when a sizable portion of a nation’s population depends on agriculture for a living. It is more accurate to argue that a country’s majority of people must rely on agriculture because it is impoverished rather than that a country is poor because the majority of its population works in agriculture.

Features of Agriculture

Agriculture is the principal industry of employment. Nearly 61 percent of the whole population is employed there. It makes up 25% of the country’s income.

Monsoon dependence: The monsoon is crucial to Indian agriculture. The production will increase if the monsoon is good, and the crops will fail if it is below normal. Floods can occasionally ruin our crops. Agriculture depends on the rainfall since irrigation systems are so poor.

Demand: Wants that are supported by a price at which they can be purchased are called demand. It is a relationship that, ceteris paribus, depicts the various quantities of a good that buyers would be able and willing to buy at various prices at a particular point in time.

Demand Schedule: It is a table that displays different quantities of a good that consumers would be willing and able to buy at different prices at a specific moment, ceterius paribus.

Demand curve: A demand schedule is a graph that displays the various quantities of a good that buyers would be able and willing to buy at various prices at a specific point in time, according to Ceterius peribus. It in turn fuels growth by increasing demand for industrial goods..

The arc slopes left to right in a descending direction.

Marshall was the frst to establish the idea of elasticity. According to the law of demand, if the price increases, fewer units will be sought for. But it’s crucial to know how much less will be expected of it. Such inquiries are crucial when formulating public policy. For instance: A price rise for salt might not have much of an effect on demand, but a price increase for TV will have more of an effect. Why? The idea of elasticity provides an answer to such queries. In economics, the concept of “elasticity” is frequently used to describe how responsive one variable is to changes in another.:

Elasticity of demand

Demand elasticity generally refers to demand’s sensitivity to price. However, there are as many levels of elasticity as there are demand determinants. The most important Elasticities are:

Determinants of demand Elasticity

According to the theory of production, the entrepreneur seeks to maximise his earnings. The goal of a proft-maximizing businessperson is to maximise production for a given amount of investment, or, to put it another way, to reduce expense.

Meaning of Production

Producing items and services with a market value is referred to as production in economics. It refers to the development of utilities. These services fall under the categories of form, time, and place utilities. The establishment of such utilities boosts the economy’s total production and redistribution of commodities and services. A commodity’s utility could rise for a variety of reasons.

Form Utility

If the physical form of a commodity is changed, its utility may increase. For instance, the utility of cotton increases, if it is converted into clothes. The other examples are processing of paddy into rice, wheat into four and butter into ghee.

Theory of Costs

In both short-run and long-run analyses, there exist different cost curves.

Short run is the duration of a given factor or factors. Equipment and business owners are typically fxed in the near term

Long run is the period over which all factors become variable.

Fixed costs: are those expenses that remain constant regardless of output or production levels.. Eg: depreciation, annual maintenance cost etc.

Variable cost: are those expenses that change depending on the volume of output eg : inputs/raw material costs, wages etc.

Short Run Cost Curves

There are seven cost concepts or curves in short run analysis. They are :

1. Total cost (TC)
2. Total Fixed cost (TFC)

Supply

Supply refers to the good being sold at a specific price and moment.

Supply is the quantity of a commodity that is actually made available for purchase at a specific price and moment. The various quantities of a commodity that would be available for sale at varying prices are referred to as the supply schedule.

Supply schedule of Rice The supply schedule for the whole market is called market supply. It is arrived at by adding the quantities supplied by all the sellers at varying prices. The increasing portion of the marginal cost curve is used to create