eChapters

In pigs, making a diagnosis of disease is no way different from that of other species. It is based on assessing the owner’s complaint, history of the farm and the ailing pig, signalment factors, detailed observations of the ailing pig, the other pigs in the group and their environment, detailed physical examination. These observation may lead to a provisional diagnosis. Further clinical tests, clinical pathology and a post-mortem examination of the dead pigs are to be taken up to confirm or refute the provisional diagnosis. Because pigs are kept in groups and owing to infectious nature of many diseases, the health of other pigs in the group should also be taken into account. History and General Examination General examination of swine is conducted without handling them. The physical examination of swine includes history taking, examination of environment, examination of group of swine, post mortem examination

Small ruminants are grazing mammals and this group consists of sheep (Ovis aries) and goats (Capra hircus).Advantage of these animals is their low cost, small size and marginal land use, which convert into high quality animal products. Sheep is a grazing animal and goat is a browsing animal. They are generally kept in flock. Therefore flock management is more important than the individual animal. Though, procedure of history taking and physical examination is almost similar to other animals, some peculiarities warrant a separate discussion. Sheep The general methods of history taking and physical examination of sheep are similar to cattle and goat starting from owner’s complaint (history), signalment of the patient, history of the ailing

History and History Taking History in medical/ veterinary terminology is a record of information about the health, illness (past and present), diet, feeding, management,preventive health schedules (vaccination,deworming, dipping, sprays) and hygiene etc. History taking and physical examination is the important step in making a correct diagnosis as it provides valuable information to determine the cause of clinical manifestations. The main purpose of taking history is to get detailed information about the animal, its activities, behavior, diet, health care practices, nutritional status, housing, development of abnormal signs and treatment given, if any. The animal may be cared by owner himself or by an attendant. In the event of animal care given by an attendant, information obtained from the attendant may not be complete and fully reliable as sometimes attendant may not provide correct events and complete picture to hide his mistakes. For obtaining right information a good rapport is to be made with the owner or the attendant. A complete history not only suggest diagnostic possibilities but can also suggest diagnostic probabilities. For example a calf or heifer is unlikely to have Johne’s disease or an adult cow is more likely to suffer from parturient paresis than a first calf heifer. History can also provide a lead for further examination or which system is to be paid special attention.

Client communication is the key to success in practice as it is an important skill to narrow down the gap between owner of the animal and the veterinarian. In other words, it is a meaningful exchange of information between attending veterinarian and the owner of the ailing animal for improving health care outcome. In client centered world (where client satisfaction is in the core of practice), communication with client  has assumed greater significance whether it is related to educating him about preventive health care; explaining about disease diagnosis, prognosis and treatment options; or explaining about chronic disease condition and its outcome. Communication also influences the wellbeing of the patient. Therefore, development of trust in client-veterinarian relationship is crucial as it not only enhances the quality of history taking but also help in better clinical diagnosis and subsequent clinical care. Importance of Communication •     Client experience and feedback is important for providing good clinical

MANIFESTATIONS OF URINARY SYSTEM DISEASES Urinary tract diseases in farm animals  are generally clinically characterized by        pain    and        difficult    urination    (dysuria);    alterations    in    urine    volume    and    its composition; rupture of the renal pelvis, bladder and urethra; and inadequacy of nervous control of urination. Clinical Signs • Dysuria and abdominal pain are common expression of discomfort owing to urinary tract diseases. Acute abdominal pain is rarely seen in cattle due to sudden distension of renal pelvis (pyelonephritis), ureter or renal infarction. Abdominal pain from urethral obstruction leading to distension of urinary bladder is clinically manifested by tail switching, kicking at belly, straining at urination and grunting. Horses with acute tubular nephrosis may show signs of renal colic (arching back, rubbing perineum and tail head). Dysuria (frequent passage of small amounts of urine with pain) is associated with cystitis, vesicle calculus and urethritis • Uremia is commonly seen in cases of acute renal failure and is manifested

MANIFESTATIONS OF GASTROINTESTINAL TRACT DISEASES Diseases of gastro-intestinal tract may be acute or chronic. Dehydration and shock are the major effects of acute and hyper acute diseases of gastrointestinal tract respectively. While major effect of chronic dysfunction of gastrointestinal tract is exhaustion from lack of food and water owing to loss of appetite (anorexia,). Other manifestations of gastro-intestinal tract diseases are abnormalities of prehension, mastication and swallowing; salivation; diarrhea: constipation and tenesmus; ileus; vomiting; hemorrhage; abdominal pain; abdominal distension; and shock and dehydration. Abnormal Prehension • Prehension   abnormalities may arise from paralysis of jaw muscles and tongue, dentition defects, mouth pain

HYPERTHERMIA Hyperthermia is a condition characterized by elevated body temperature due to increased heat production or absorption, or reduced heat loss   owing to physical cause. Etiology • Physical causes such as exposure to high environmental temperature, severe and prolonged physical activity especially in hot humid condition or    animals    confined    with    poor    ventilation. • Spontaneous hemorrhage in hypothalamus can cause neurogenic hyperthermia. • Severe dehydration affects the heat loss by evaporation and thus can lead to hyperthermia. • Too much muscular activity.

The concept of health and diseases is essential to veterinary medicine. Understanding    of    such    concepts    provides    the    basis    of    disease    classification. But     these     concepts     are     rarely     given     precise     definition     in     the     textbooks     of veterinary medicine. HEALTH Health is generally considered as absence of the disease. But it is an incomplete definition.    The    concept    of    health    can    be    split    into    five    categories    viz.    health as normality, health as biological function, health as homeostasis, health as physical and psychological wellbeing, and health as productivity including reproduction. Therefore an animal is stated to be healthy only when it is in a complete state of physical, social and mental wellbeing and not

The word “Veterinary” has originated from Latin “veter?n?rius” concerning draught animal and “Medicine” from Middle English, Middle French, Old French and Latin words meaning “healing”. Veterinary medicine is the offshoot of the medicine dealing with the diagnosis, treatment and prevention of diseases/disorders of all animal species including domestic, wild and exotic, companion, draught and pack animals. CONTRIBUTION OF VETERINARY MEDICINE • Reduction in number of cases of tuberculosis and brucellosis in human. • Development of safe and effective vaccines to prevent and control of animal diseases such as Rinderpest, Foot and mouth, Hemorrhagic septicemia, Black quarter,  Pox, Parvo, Distemper,

Financial data visualization is the process of turning financial information into visual formats like charts, graphs, and dashboards. It helps finance teams interpret large datasets quickly, spot patterns or anomalies, and make betterinformed business decisions. Data visualization in finance is a craft of transforming complex financial numbers into accessible visual forms. Visual formats such as charts, graphs, dashboards, maps, and interactive displays serve as lenses through which data becomes visible and understandable. Finance practitioners navigate risk profiles, asset performance, expense trends, cash flows, and portfolio behavior more effectively when raw inputs become visual stories. Creation of visual representations hinges on careful selection of chart types, scaling, color, and annotation. Communicating precise meanings through visuals demands an understanding of data relationships, temporal changes, distributions, and comparisons.

Financial modeling using Python has emerged as a highly effective approach for modern finance, combining the flexibility of programming with the analytical rigor required for accurate forecasting and decision-making. Python is particularly valuable because it can handle large datasets, automate repetitive tasks, and integrate advanced statistical and machine learning techniques into financial models, surpassing the limitations of traditional spreadsheet-based approaches. Its extensive libraries, such as pandas, NumPy, scikit-learn, and matplotlib, make it an ideal tool for building robust and dynamic financial models. The foundation of financial modeling in Python begins with data acquisition and preprocessing. Financial data is often sourced from databases, APIs, or CSV files, and Python provides efficient tools to import and clean this information. The pandas library allows analysts to create dataframes, which are flexible structures for organizing, filtering, and transforming data. Preprocessing tasks, such as handling missing values, normalizing variables, and converting date formats, ensure that datasets are ready for accurate modeling. This step is critical, as errors or inconsistencies in the data can lead to incorrect forecasts and flawed financial decisions. Once the data is prepared, Python enables comprehensive exploratory data

SQL, or Structured Query Language, provides a powerful framework for querying, aggregating, and transforming financial data stored in relational databases, which is essential for constructing accurate and dynamic financial models. Unlike spreadsheet-based models, SQL enables automation, repeatability, and scalability, making it particularly useful when dealing with complex datasets or real-time financial information. The first step in financial modeling with SQL is understanding the structure of the database. Financial data is often stored across multiple tables, including transactions, accounts, customers, and market data. Effective financial modeling requires familiarity with relational database concepts such as primary keys, foreign keys, and normalization. By using SQL joins, analysts can merge these tables to create a unified dataset that captures all necessary information, such as revenues, expenses, and cash flows, which are essential for forecasting and valuation exercises. Data extraction in SQL

Financial modeling is one of the most highly valued, but thinly understood, skills in financial analysis. The objective of financial modeling is to combine accounting, finance, and business metrics to create a forecast of a company’s future results. A financial model is simply a spreadsheet, usually built in Microsoft Excel, that forecasts a business’s financial performance into the future. The forecast is typically based on the company’s historical performance and assumptions about the future and requires preparing an income statement, balance sheet, cash flow statement, and supporting schedules (known as a three-statement model, one of many types of approaches to financial statement modeling). Financial modeling skills are essential for anyone pursuing a career in finance. Models synthesize a vast amount of information to help users make better investment and business decisions. The modeler’s technical acumen

Dashboards play a crucial role in the fields of Machine Learning and Artificial Intelligence by providing a visual and interactive interface through which complex data, model outputs, and performance metrics can be easily understood and monitored. In data-driven environments, where vast amounts of information are processed continuously, dashboards serve as an essential bridge between technical systems and human decision-making. They transform raw data and analytical results into meaningful visual representations, enabling users to interpret insights quickly and effectively. In the context of Machine Learning and Artificial Intelligence, dashboards are used at multiple stages of the workflow. During data exploration and preprocessing, dashboards help visualize trends, patterns, and anomalies in datasets, allowing data scientists to make informed decisions about feature selection and data cleaning. Once models are developed, dashboards

A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. In image recognition, a machine learning model can be taught to recognize objects - such as cars or dogs. A machine learning model can perform such tasks by having it ‘trained’ with a large dataset. During training, the machine learning algorithm is optimized to find certain patterns or outputs from the dataset, depending on the task. The output of this process - often a computer program with specific rules and data structures - is called a machine learning model. Machine Learning Model is a computational program that learns patterns from data and makes decisions or predictions on new, unseen data. It is created by training a machine learning algorithm on a

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems. A technique that helps to convert improper data into meaningful data. Machine Learning is datadriven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output. Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data

SQL, or Structured Query Language, is a fundamental component in the domains of Machine Learning and Artificial Intelligence, serving as a powerful tool for managing, retrieving, and manipulating data stored in relational databases. In an era where data drives intelligent systems, SQL plays a crucial role in preparing and organizing datasets that form the backbone of machine learning models and AI applications. Its ability to efficiently handle large volumes of structured data makes it indispensable for data scientists, analysts, and engineers working in these fields. Originally developed in the 1970s at IBM by researchers Donald D. Chamberlin and Raymond F. Boyce, SQL was designed to provide a standardized way to interact with relational databases. Over time,

Python has emerged as one of the most popular and versatile programming languages in the contemporary world of computing. Developed by Guido van Rossum and first released in 1991, Python was designed with a clear emphasis on simplicity, readability, and ease of use. Its syntax closely resembles natural language, making it an ideal choice for beginners as well as experienced programmers. Over the years, Python has evolved into a powerful, high-level language that supports multiple programming paradigms, including procedural, object-oriented, and functional programming. One of the defining features of Python is its readability and clean structure, which allows developers to write clear and concise code. Unlike many other programming languages that rely heavily on complex syntax and punctuation, Python uses indentation to define code blocks, promoting better organization and reducing the likelihood of errors. This design philosophy not only enhances code clarity but also encourages good programming practices,

Workflow Optimization is the process of improving the way work is done by making it faster, more efficient, and more organized. In every organization, work is completed through a series of steps. These steps together form a workflow. If the workflow is not properly organized, it can lead to delays, errors, confusion, and increased costs. Workflow optimization helps in improving these processes so that work can be completed in less time, with fewer errors, and with better results. In the modern digital world, workflow optimization has become very important because organizations are handling large amounts of data and complex tasks. With the help of AI tools and automation software, workflows can be optimized and automated. Workflow optimization

Task Automation is one of the most important applications of Artificial Intelligence and modern digital technology. It refers to the process of using technology, software, or AI tools to perform repetitive and routine tasks automatically without continuous human involvement. In many workplaces, people spend a large amount of time doing repetitive tasks such as sending emails, entering data, preparing reports, scheduling meetings, organizing files, and updating records. These tasks are necessary but time-consuming. Task automation helps in completing these tasks automatically, allowing people to focus on more important and creative work. In the modern digital world, automation has become an essential part of offices, businesses, education systems, banks, hospitals, and even personal life. Earlier, automation was limited to industries and manufacturing, but now automation is used in office work, communication, marketing, research, and data management. With the help of AI tools, automation has become smarter

Artificial Intelligence (AI) tools have rapidly transformed the way we work, learn, communicate, and solve problems in the 21st century. From automating routine tasks to assisting in complex decision-making, AI tools are becoming an essential part of modern life and professional environments. These tools use technologies such as machine learning, natural language processing, computer vision, and data analytics to perform tasks that traditionally required human intelligence. As a result, AI is not only increasing productivity but also changing the nature of work across industries. AI tools are software applications or platforms that use artificial intelligence to perform specific functions such as content creation, data analysis, customer support, language translation, image generation, video editing, coding assistance, and workflow automation. Today, AI tools are used in education, healthcare, business, marketing, finance, engineering, journalism, and many other fields. For example, students use AI tools for research, writing, and learning new concepts; businesses use AI for customer service chatbots, data insights, and marketing automation; and designers use AI for image and video creation.

Business efficiency refers to the ability of an organization to use its resources— such as time, money, labor, technology, and materials—in the most effective way to achieve maximum output with minimum waste. In simple terms, business efficiency means doing work in the best possible way with the least amount of effort, cost, and time while maintaining high quality. Efficiency is not only about working faster; it is about working smarter. A business is considered efficient when it produces more results with fewer resources. For example, if a company can produce 100 products in 5 hours instead of 8 hours by using better machines or automation, then the company has improved its efficiency.

AI-based content creation refers to the use of Artificial Intelligence technologies to generate, edit, optimize, and publish content automatically or semi-automatically. Content can include text, images, videos, audio, presentations, reports, emails, and social media posts. AI content creation tools use technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Deep Learning to create content that looks like it was created by humans. In the modern digital world, content plays a very important role in education, business, marketing, communication, and entertainment. Earlier, content creation was completely manual and required a lot of time, effort, and human resources. Writers, designers, editors, and analysts had to work for many hours to create a single report, article, or presentation. But now, with the help of AI tools, content can be created in minutes instead of hours

Productivity automation refers to the use of technology, software, and artificial intelligence to perform tasks automatically with minimal human effort. The main goal of productivity automation is to save time, reduce manual work, increase efficiency, and improve the quality of output. In simple words, productivity automation means using machines and digital tools to do work faster and more accurately. Productivity automation is used in many areas such as business operations, communication, data management, marketing, education, and personal productivity. For example, sending automatic emails, scheduling meetings, generating reports, managing tasks, and analyzing data are all examples of productivity automation. The concept of productivity automation is based on the idea that repetitive and routine tasks should be handled by machines so that humans can focus on creative,

Cost analysis enables organizations to understand cost behavior, improve efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis

efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis for meaningful cost analysis and modeling.q

Budgeting is a fundamental component of financial modelling programs and data analytics, serving as a structured approach to planning, allocating, and controlling financial resources within an organization. It involves estimating future revenues and expenses based on historical data, current trends, and strategic objectives. When combined with data analytics, budgeting becomes more dynamic, accurate, and responsive, enabling organizations to make informed financial decisions and adapt to changing conditions. The budgeting process begins with data collection and preparation. Historical financial data, including past revenues, operating costs, and capital expenditures, is gathered from various sources such as accounting systems and databases. Data analytics tools help clean, organize, and validate this information, ensuring consistency and reliability. This step is crucia

Financial modeling programs integrated with data analytics using SQL provide a strong foundation for managing, analyzing, and transforming financial data in a structured and scalable manner. SQL, or Structured Query Language, is essential in environments where large volumes of financial data are stored in relational databases, enabling analysts to build reliable and data-driven financial models. Its ability to efficiently query, aggregate, and manipulate data makes it a critical tool in modern financial analytics. The process begins with understanding the database architecture. Financial data is typically distributed across multiple tables such as transactions, accounts, customers, budgets, and market indicators. SQL enables the integration of these datasets through joins, allowing analysts to

Financial modeling programs integrated with data analytics using Python have transformed the way financial analysis, forecasting, and decision-making are conducted. Python provides a powerful, flexible, and scalable environment that enables analysts to move beyond traditional spreadsheet limitations and build sophisticated models capable of handling large and complex datasets. Its growing adoption in finance is driven by its ability to combine data processing, statistical analysis, automation, and visualization within a single ecosystem. The foundation of Python-based financial modeling lies in efficient data handling and preprocessing. Financial data is often sourced from multiple channels, including databases, APIs, and flat files, and must be cleaned and structured before analysis. Libraries such as pandas and NumPy provide robust tools for organizing data into structured formats,

In business people regularly have to deal with situations where they have to make some decisions after analyzing some data. In such cases they often build a spreadsheet model of their problems. Financial Modelling using spreadsheets like MS Excel are used in many fields like financial analysis, risk analysis, valuations, investment management, credit analysis, scenario analysis, budgeting, sales forecast, financial projections, project evaluation, etc. Along with the use of Excel as a tool for modelling, skills in recording and using Macros in Excel is also required. Excel modeling remains one of the most widely used approaches in financial modeling and data analytics, offering a flexible and intuitive platform for analyzing financial data, building forecasts, and supporting decision-making. Its popularity stems from its accessibility, powerful built-in functions, and ability to create structured models without requiring advanced programming

Kudumbashree, is the flagship poverty eradication and women’s empowerment programme, of the Government of Kerala, and is one of India’s most successful community development missions. Launched in 1998 by the Government of Kerala in collaboration with NABARD and the Government of India, Kudumbashree operates through a unique three-tier community network that connects millions of women across the state. Its model- built on economic empowerment, social inclusion, and participatory governance- has become a benchmark for women-led development both nationally and internationally. At the foundation of Kudumbashree lies its grassroots structure: the Neighbourhood Group (NHG) of 10–20 women; the Area Development Society (ADS) at ward level; and the Community Development Society (CDS) at the panchayat level. This federated network enables women to mobilize

2.1 Mulkanoor Cooperative Society: An Introduction The Mulkanoor Cooperative Rural Credit and Marketing Society, established in 1956, in Mulkanoor village of today’s Telangana state of India, stands as a beacon of rural transformation. Post-independence, Shri A.K. Vishwanatha Reddy- a freedom fighter- envisioned a better future for farmers. His vision materialized through the formation of the Mulkanoor Cooperative Society, initially with 373 members and a modest share capital of ?2,300. The society has now grown to encompass 7,650 members, ?18 crores in share capital, and ?45 crores in thrift deposits, and offers a wide

Agriculture remains the primary livelihood for much of South Asia, underpinning food security, rural incomes, and economic stability. Despite its pivotal role, the sector has historically been constrained by low productivity, fragmented supply chains, significant post-harvest losses, and limited access to markets and institutional finance (FAO, 2023).Over the last decade, a rapid expansion of technology-driven agritech startups across South Asia- particularly in India- has sought to address these systemic gaps. By deploying artificial intelligence (AI), the Internet of Things (IoT), machine learning (ML), blockchain, drones, and renewable energy, these enterprises are reshaping agricultural value chains, enhancing resilience, and creating measurable socio-economic impact. Following are some of the promising areas where startups have shaped rural economies

Abstract

This chapter covers the main theories and academic perspectives used to study the role of different organizations in which they explain their vision, mission, object functioning, etc.

Abstract

This chapter explains the cooperation which create social relations that help individuals achieve goals that they could not achieve alone. They also help farmers benefit from economies of scale and reduce risks, and empower their members economically and socially by involving them in decision-making processes.

Abstract

This chapter explains SWOT analysis, a tool for corporate planning to evaluate how an organisation compares with its rivals. It stands for strengths, weaknesses, opportunities, and threats. Albert Humphrey is traditionally considered the originator of the plan in the 1960s, but this claim is disputed. No one agrees on who created it. It has become useful for differentiating from and finding a niche within the larger market. It is also called the SWOT Matrix. SWOT Analysis is a tool that can apply outside of business to analyse a person’s status against their competitors. The tool considers both internal and external factors. The terms “weaknesses” and “strengths” relate internally. The former is a characteristic of a company or other entity that gives it a disadvantage over rivals. The latter is a trait of that same entity, which gives it an advantage over its rivals. For external factors, “opportunity” are aspects of the larger environment that the entity can exploit to its benefit. However, “threats” are also realities in the larger environment, which could harm the entity.

Abstract

Financial analysis evaluates the performance and suitability of enterprises, projects, budgets, and other financial-related institutions. It determines if a business is secure, financially sound, liquid, or profitable enough to warrant a financial commitment. Accurate records and financial statements provide the essential information for analyzing the financial situation and trends of a farm operation. A financial analyst examines the income statement, balance sheet, and cash flow statement.

Abstract

Many obstacles hinder agriculture credit in rural regions: insufficient funding, failure to reach vulnerable rural residents, especially marginal and small farmers, and lack of local focus in agriculture credit distribution. India adopted a “Social and Development Banking” policy after nationalizing commercial banks in 1969. This policy aimed to solve some of these issues and boost the supply of formal credit to the agriculture sector. Rural communities would benefit from more loans at competitive interest rates due to this policy. In the 2000s, agriculture credit grew annually by 17.6%, much higher than the 2.6% growth rate in the 1990s. The increase in agriculture credit had three distinct features: indirect financing, a sharp rise in loans of Rs. 10 crore and above, and growing availability of agriculture credit through urban bank branches. The revival of agriculture lending in the 2000s mostly benefited small farmers and marginal farmers

Abstract

Rural indebtedness is an old problem in India. In the 19^th century commercial banking was absent in rural areas and farmer were entirely in the hand of usurious moneylender. Since the British rule, the central government has been trying to extend institutional lending to the rural agriculture sector. To provide credit to the agricultural sector, a multi-agency approach involving co-operative banks, scheduled commercial banks, and RRBS has been used in India. Adequate and timely loan availability at reasonable rates is the basis for agricultural finance policy. The institutional structure for agricultural lending has improved over time in terms of scope and reach. India’s higher education institutions have been focusing their efforts on advancing agricultural finance.

Abstract

Agriculture, like any other economic activity, needs finance. Crop productivity in India is low due to traditional farming practices and agricultural production must increase to achieve self-sufficiency and save foreign exchange. Therefore, credit is vital for enhancing agricultural production. Credit helps develop fixed farm assets and can be classified into short-term loans, medium-term loans, and long-term loans, according to studies on the relationship between agricultural growth and credit availability. However, credit withdrawal might cause a decline in conditions, while credit provision with stagnant production conditions won’t improve output. Since production conditions are dynamic, loan requirements would grow from year to year even if input stay constant.

Abstract

Most farmers in India lack capital. They need credit at the right time, from the right agency, and in adequate amounts to be productive. From the farmer’s perspective. When a farmer applies for a loan from an Institutional Financial Agency (IFA), the lender must be convinced of the 3 R’s, 5 C’s, and 7 P’s, which indicate the economic viability of the proposed investments.

Abstract

This chapter examines how agricultural credit is a vital input for all agricultural development programmes. Private moneylenders used to be the main source of agricultural credit. However, this source was insufficient, costly and oppressive. To overcome this, a multi-agency approach involving cooperatives, commercial banks and regional rural banks has been adopted to provide cheaper, timely and adequate credit to farmers.

Abstract

Agricultural development interventions need agricultural financing to increase productivity. This study investigates what factors influence farmers’access and use of agricultural loans, what challenges they face in obtaining and repaying loans, and how financial support affects their income outcomes. The study also suggests some policy actions to improve the supply and performance of agricultural loans for farmers.

1.1. Meaning

Agricultural finance generally means studying, examining and analysing the financial aspects pertaining to farm business, which is the core sector of India. The financial aspects include money matters relating to production of agricultural products and their disposal.

1. Introduction
It is the need of the hour that disposal of radioactive wastes be taken seriouslyand given appropriate attention. We house around one million gallons of liquidradioactive waste awaiting remediation at U.S. Department of Energy’s HanfordReservation alone (Parshley & McDermott, 2021). Not only the numbers are
but the increasing demand of nuclear energy paves the way forgeneration of even more radioactive waste in future. The conventional methodshave certain limitations. Ion-exchange resins have reported to bedegradedunder high radiation and chemical precipitation has proven to be ineffectivein treating low concentration of radionuclides found in groundwater, posingrisk for centuries (Zhang & Liu, 2020). Remediation methods including soil

1. Introduction
The environment and human health are seriously threatened by contaminants,both organic and inorganic, whose prevalence and persistence have beensteadily rising in recent years. In addition to propelling economic and scientificadvancement, the industrial revolutions of the 19th and 20th centuries have alsoled to widespread pollution (Wuana & Okieimen, 2011; Singh et al., 2013).Industrial activity has increased trace metal concentrations in the environment,with sediments acting as key sources and reservoirs for heavy metals. Thesemetals build up in soils and aquatic bodies as a result of scouring, erosion,rainfallleaching, wastewater discharge, and atmospheric deposition (AlonsoCastillo et al., 2013). Additionally, toxic contaminants like phenols, industrialeffluents, domestic wastewater, agricultural runoff, fly ash, fertilizers, andwaste from insecticides, pesticides, and pharmaceutical industries infiltratethe environment and food chain via mechanisms such as bioaccumulation,biomagnification, and bioconcentration (Arora & Bae, 2014; Azubuike et al.,2016).

1. Introduction to Biomedical Waste (BMW)
The waste produced at different medical facilities, including hospitals,laboratories, research centers, mortuaries, and autopsy centers is considered asBiomedical Waste (BMW). Some of the BMW is hazardous whereas some arenon-hazardous in nature. Hazardous waste includes, Infectious, Pathological,Microbiological, Pharmaceutical, Chemical, Cytotoxic and Radioactive wastematerial as described by World Health Organization (Fig. 1). Healthcarepersonnel and the general public are at risk from physical, chemical, andmicrobiological effects of hazardous waste. On the other hand, the nonhazardousBMW which are not harmful, include packaging material made upof plastic & thermocol, cardboards, paper waste etc. (Capoor & Parida, 2021).

1. Introduction
Plastic pollution is a global problem that requires immediate attention due toits negative impacts on ecosystems, infrastructure, (MacLeod, M., Arp et al.2021) society, and the economy (Honingh, D. et al.,2020).In terms of absolute emissions, we find that South-eastern part of Asia, andSub-Saharan part of Africa possess the greatest quantities of plastic pollution,with India emitting the most (9.3 Mt year−1), or about one-fifth of the world'stotal plastic pollution (Joshua W. Cottom et al.).
Future regulations pertaining to the manufacturer, usage, and getting ridof plastics will be influenced by the discussions for a worldwide plasticpollution treaty. A baseline with great resolution of waste flows and sourcesof plastic emissions will be useful for the stakeholders in order to identify
pollution hotspots and the reasons behind them (United Nations EnvironmentProgramme). Furthermore, in addition to the production of municipal waste,the pandemic of coronavirus illness has raised the usage of disposable polymerproducts, masks, tissues, gloves, and additional personal protection equipment,which made the plastic pollution disaster worse (De-la-Torre GE et al., 2021).Wasted plastics can break down and fragment into three different sizes: 1mm to 5 mm big microplastics, 1 μm to 1 mm microplastics, and 1 nm to1 μm nano plastics, (Waller et al., 2017; De-la-Torre 2020; Atugoda et al.,
2022) being exposed to ultraviolet (UV) light and other processes that causeenvironmental damage

1. Introduction
The term “plastic” is derived from the Greek word “Plasticos" whichmeans to be capable of molding into various sizes and shapes. Daily usedsynthetic plastics are categorized based on their physical and chemicalproperties, such as thermoplastic and thermosetting. (i) Thermoplastics: Astheir names suggest, thermoplastic polymers melt and turn into a plastic orrubbery state when exposed to heat. They have various numbers of polymericchains and are the most widely used plastics in day-to-day life. Examples ofthermoplastics are Polystyrene(PS), Polyvinylchloride (PVC), Polypropylene(PP), Polyethylene Terephthalate (PET), Polyethylene (PE) etc. (Massy,2017), (Sharma et al., 2017). (ii) Thermosetting polymers: As the namesuggests, they set or harden permanently when heated (Massy, 2017). Due totheirstrong crosslinked structure, they are non-recyclable polymers. Examplesinclude Polyurethanes(PUR), Phenol formaldehyde, Melamine formaldehyde,and Polyester Resin (PR) (Sharma et al., 2017). Different polymers and theirapplications are illustrated in Fig. 1.

1. Introduction
Since plastic was discovered, global plastic production for industrial scale usages has grown exponentially, causing a tremendous increase in plastic waste from a few tons in the 1950's to almost 400 million tons in 2018 (Syberg et al. 2021). Plastic is non-biodegradable and ubiquitous.

Environmental degradation of plastic operates through a mechanism ofphysical fragmentation, photodegradation and microbial degradation (Wright
et al., 2020), resulting in fragmentation of larger plastics into smaller plasticparticles, also called microplastics and nanoplastics (MNPs). These debris areserious environmental threats (Hartmann et al., 2019).

1. Introduction
Pharmaceutical pollution has emerged as a significant global concern, raising alarms about its potential impacts on ecosystems and human health. The journey of pharmaceutical compounds from their point of use to contamination of water bodies reveals a complex and interconnected web of anthropogenic activities. From manufacturing plants to individual households, these pollutants persistently infiltrate aquatic systems, creating a growing