eChapters

Multistage Sampling

Multistage sampling is a form of random sampling where samples are selected in a sequence of stages. Where each sample is drawn from the exclusive previously selected samples. This sampling is not done once but at multiple stages. It differs from cluster sampling in the sense that cluster uses all the subjects within a cluster whereas multistage selects a sample from within the selected group. Groups are randomly chosen from a population; subgroups from these groups are randomly chosen and then randomly chosen to be surveyed.

This is an extension of cluster sampling, which is carried out in multiple stages say two, three or four stages. In the first stage, the universe is divided into some clusters from which certain clusters are selected at random at the first stage samples. In the second stage, the selected first stage samples are again subdivided into some clusters from which again certain clusters are selected at random as the second stage samples. In the third stage, the selected second stage samples are again subdivided into some clusters from which certain clusters are again selected at random as the third stage samples.

Q.1.    What is split plot experiment ?

    Answer
    
    If one has to study two type of factors say A and B in the same experiment where A requires larger plot size and B requires smaller plot size then the structure of the experiment will be that of plots within blocks are known as split plots and blocks within replication are known as main (whole) plots. Such a layout of the experiment is known as split plot experiment.

Stratified Sampling (Meaning and Concept)

The word ‘stratify’ comes from the Latin word, meaning ‘to make layers’, thus in stratified sampling the whole population is considered to be classified into some mutually exclusive layers or subgroups or classes also called strata. The strata do not overlap and they constitute the whole population.

Why the population is considered to be divided into different strata (or layers or subgroups or classes)?

Q.1.    Define strip plot design ?

    Answer
    
    There is a variant of the split plot design in which both factors A and B will be applied to the larger strips by dividing the experimental field into as many rows as the number of levels of one factor A and into as many columns as the number of levels of second factor B. Then the two factors will be applied at random to the rows and columns. Such a layout is known as strip plot design.

Systematic Sampling (Meaning and Concept)

In this chapter, we are going to discuss the systematic random sampling technique. This is a random / probability / quantitative sampling method. In our previous chapter, we discussed the stratified random sampling technique. We have said that this technique is used when the population is heterogeneous and needs to be stratified into homogeneous groups. We have also discussed steps to be followed when using stratified random sampling as well as the strengths and limitations of stratified random sampling. Systematic sampling is a commonly employed technique if a complete and up-to-date list of the sampling units is available. The procedure involves choosing the very first object arbitrarily and the remaining members of the sample will be selected naturally according to any pre-decided sequence containing regular spacing of units. In this method, units of the population are numerically, geographically, and alphabetically arranged either in ascending or descending order (generally ascending order is preferred). When the ordering of the elements is related to the characteristic of interest,

Test for Significance for Population Mean
    
Test for Significance of Population Proportion

Preliminaries and Test Based on N. P. Lemma
    
More on Testing of Hypothesis

Introduction

The rapid growth and socio-economic upliftment of a developing country are directly related to the intensity of industrial growth of that country (Bastian et al., 2019; Kniivila, n.d). The emergence of new enterprises is the main source of a healthy and growing industrial ecosystem. They represent 99% of businesses in developing countries and account for two-thirds of employment creation (Secretary-General and UNCTAD, 2022). Enterprises hence create employment, drive inclusive growth, promote innovation, and improve socio cultural conditions along with addressing sustainable development goals- No poverty(SDG-1), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10)of the UN’s 2030 agenda (Forbes, 2022; Secretary-General and UNCTAD, 2022). Their innovative outputs in the form of goods and services, address local as well as global requirements of the customer market and bring them on the path of sustainability (Szopik-Depczynska et al.,2018; Rivera et al.,2020). The entrepreneurs have a long vision of success, engaging their primary activities with No poverty (SDG-1), Zero hunger(SDG-2), Gender equality(SDGs-5), Decent work and economic growth(SDG-8), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10), Responsible consumption and production(SDG-12), Climate action(SDG-13), Partnership for goals(SDG-17) and addressing challenges of society and the environment in return for optimal economic profit (Alagpuria,2021; Lammers et al.,2022; Secretary-General and UNCTAD,2022; Vig,2021; Del-Aguila Arcentales et al.,2022; Sadiq et al.,2022).

Accurate crop-yield forecasting supports food-security planning, resource allocation and farm decision-making. Yield is influenced by interacting factors such as weather, soil, irrigation, fertilizer and pesticide use, making reliable prediction challenging. This chapter presents an algorithmic framework using historical agricultural and environmental data. It covers cleaning, missing-value treatment, categorical encoding, normalization and feature extraction. Decision Tree, Gradient Boosting, AdaBoost and XGBoost regression models are evaluated individually and combined through soft voting. Performance is measured using standard prediction-error metrics. The case study demonstrates that ensemble modelling can improve accuracy and robustness, helping farmers and planners optimize inputs, anticipate production and strengthen agricultural productivity.

Introduction

A revolution is coming to the more than a century-old automobile industry. Electric vehicles (EVs) are viewed as a solution to a variety of problems, including reducing greenhouse gas emissions, fostering sustainable economic growth, improving air quality, and reducing the importation of fossil fuels. The demand for electric automobiles in India is rising. Incentives and programs have been launched by the federal and state governments to promote the usage of electric vehicles, and there are also regulations and standards in place. A consumer is a person or group that plans to purchase something, actually does so, or utilizes it for primarily social, family, domestic, or other non business or entrepreneurial activity-related objectives.

An asset that is an important criterion for evaluating the status of the consumer economy can be used to estimate consumer savings.

The concept of electric cars has been around for a while, but in the past ten years, it has gained a lot of attention due to the expanding carbon footprint and other environmental implications of fuel-powered cars. In contrast to an internal combustion engine, which generates power by burning a mixture of fuel and gases, an electric vehicle (EV) is propelled by an electric motor. When electricity was one of the favored sources of motor vehicle power in the late 19th century, electric vehicles (EVs) first made their appearance, providing a degree of comfort and usability that gasoline cars of the day were unable to equal.

Introduction

Virtual supply chain management is the digital representation of inventory data that the managers manage from anywhere in the world. In the 1980s, when the proliferation of computers gave more importance to the human resource, f inance, and manufacturing units communicating with each other for better quality work, that led to enterprise resource planning, and slowly each task of supply chain management started virtualizing in its full length.

A virtual supply chain has advantages such as better inventory control, shorter lead times, quicker order fulfilment, increased supply chain visibility, more accurate demand forecasts, and higher customer satisfaction. It gives businesses the ability to make the most use of their resources, cut expenses and lessen waste throughout the supply chain.

However, putting in place a virtual supply chain calls for thorough planning technology integration, and network-wide process alignment. Overcoming obstacles like data security, interoperability, and change management are all part of it. To fully reap the rewards of a virtual supply chain, businesses must also invest in technical infrastructure, training courses, and partnerships.

Introduction

The phrase “sustainable” has been used so much in recent years-and frequently so imprecisely—that for many people, it has become meaningless or confused, more of a rhetorical chant than a source of useful advice. Lester Brown, the founder of the World Watch Institute, defined a sustainable society as one that can meet its requirements without compromising the opportunities for future generations. This definition is the one that we come across most frequently. Brown wrote this description in the early 1980s. The Brundtland Report, a 1987 United Nations report, addressed this issue and defined sustainable development as development that satisfies present demands without jeopardising the ability of future generations to satiate their own. Despite their obvious limitations speaking simply of human wants, for example, without reference to the natural world-these are significant moral exhortations. They do serve as a helpful reminder that it is our duty to provide our kids and grandkids with an equally abundant planet as we did, but they offer no guidance on how to truly create a sustainable society. When air, water, and minerals interact with living things to fill a specific region, it is referred to as an ecosystem (Tansley, 1935). This word was first used in 1935. Before then, the main goals of land management had been to provide a single item, like food or lumber, or to conserve a single, iconic species.

Every practical problem need not have a documented solution. Most of the times to solve a practical problem, we need to apply our experience, creativity and experiment with the novel solutions in order to address the problem which have no readymade solution. Under such circumstance, we will apply our knowledge and experience in selecting, planning, experimenting and evaluation of the possible solutions. This type of problem solving approach involves action research.  Action research is a type of social research that aims at gaining an insight into the problem by learning from experiences gained in an effort to solve the problem. Here, action itself is a source of knowledge.

Q.1 In applying varying probability sampling for information of population total of X, it is required to know the values of selection probabilities Pi (i = 1, 2, 3,..., N).

Q.2 The technique of “Double Sampling” was first time formulated by Jayman, (1938) in connection of collecting information on strata sizes in stratified random sampling scheme.

Age old practice of photography was taken with still camera in parallel direction, not aerially scan or snapping which really present wider prospective of any object or places in rearranging or planning skilfully in two dimensional working. Earlier aeroplane played a important role in over head condition to capture snaps but again accuracy and angle of camera limited the works because these photographs were diagonally or trigonally not matched the reality of ground.

Aptitude questions related to ages are commonly asked in various competitive exams, so you should definitely solve few questions for exercise. Now let us go through the different types of statements used in this topic.

Let us find the age of Rakesh by understanding his statement. Let us take the kid’s current age as X. So as per statement given by him, X = 1.5(X – 10). Solving for X, we get the value of X as 30. Hence current age of Rakesh’s son is 30 years.

Precision agriculture depends on information collected through ground sensors, satellites, IoT systems and other connected devices. However, these data sources differ in spatial resolution, timing, quality and technical compatibility. This chapter introduces a computational framework for acquiring, harmonizing and analysing heterogeneous agricultural data. Preprocessing methods address noise, missing values and inconsistencies, while machine learning, deep learning and ensemble models identify useful environmental, spectral and temporal patterns. Applications include crop-health assessment, soil-moisture estimation, yield prediction and climate-resilience planning. Edge and cloud computing support scalability and real-time operation. Integrating multiple data sources can improve model accuracy, robustness and the practical usefulness of intelligent farming systems.

Introduction

Indian economy is basically an agrarian economy. In spite of all challenges and difficulties faced over the years, the sector still serves as the lifeblood of the nation, by standing as the major source of livelihood for over half of the population by guaranteeing food security for its teeming millions. A significant contribution to the GDP of the nation is still ensured by the agriculture sector. The development of a nation highly centres on its primary agriculture sector and Indian economy have a much diverse dependence on the agriculture sector in terms of its growth and development. The nation straightaway necessitates a tremendous shift entirely to sustainable agricultural practices in its development trajectory.

Despite its undeniable significance and praised role, Indian agriculture currently faces multifaceted challenges, which significantly restrict the production and productivity level of the sector as a whole and the farmers in particular. Its high time that the Indian agriculture sector, which stands as the backbone of the nation needs to go through a looming transformation. The industry 4.0 revolution is a vibrant platform for the sector to make a complete transformation. The traditional face of the sector will transform towards a modern makeover by taking up technological advancement and digitalisation. The wider transformative opportunities will ensure a return of the Indian agriculture sector to the forefront in the nations growth and development.

Introduction 
Chapter 13 delves into the field of agriculture extension and communication, emphasizing its role in disseminating knowledge and fostering rural development. This chapter explores how effective extension education and communication strategies contribute to advancing agricultural practices, improving food security, and addressing global warming challenges. The chapter begins with an examination of extension education and rural development, focusing on how extension services bridge the gap between research and practice. It highlights the role of extension services in providing farmers with access to new technologies, best practices, and resources that enhance agricultural productivity and sustainability. Communication and technology transfer are crucial for effective extension work. This section discusses various communication methods and technologies used to transfer knowledge to farmers. It includes the role of information and communication technology (ICT) in facilitating access to agricultural information and supporting the adoption of innovative practices. Farmer Field Schools (FFS) and capacity building are explored as practical approaches to extension education. The chapter outlines how FFS provides handson training and experiential learning opportunities for farmers, enabling them to develop practical skills and knowledge that improve their farming practices. The importance of agriculture extension and communication in food security is emphasized, highlighting how these efforts contribute to increasing agricultural productivity, improving food quality, and ensuring a stable food supply. The chapter discusses how effective extension services can help address food security challenges by promoting sustainable farming practices and supporting farmers in adapting to changing conditions. The impact of agriculture extension and communication on global warming is also considered. This section examines how extension services can contribute to mitigating climate change by promoting practices that reduce greenhouse gas emissions and enhance environmental sustainability. Modern concepts in agriculture extension and communication are reviewed, showcasing recent advancements and innovative approaches in the field. The chapter discusses how new technologies and methodologies are transforming