eChapters

1.0 INTRODUCTION
 
Coriander is a native of southern Europe and Asia Minor. In a treatise, Farrell (1999) has described coriander as indigenous to the Near East and Mediterranean region of North Africa and Southern Europe. According to early Sanskrit writings, coriander was known as far back as 5000 BC for its cultivation in India. Persia grew coriander 3000 years ago for adding fragrance to hanging garden of Babylon and seeds were found in Egyptian tombs of 960 BC. It has been used as an ingredient to traditional and folk medicine in the Middle East and Asia for more than 2000 years. There is archaeological evidence of coriander cultivation from Early Bronze Age in Macedonia (Fragiska, 2005)

The disease appears as tumour like swelling or galls on leaf veins, leaf stalks, peduncles, stems and on fruits. The galls on the vein give a swollen and hanging appearance to the leaves. These are elongated, looks glossy in beginning but later rupture to become rough and corky. When stems remain succulent, numerous tumours may form on them. In severe case, the plant may kill prematurely.

Etiology

A turkey coronavirus (TCV).

Hosts

Turkeys of all ages, but the disease is mostly observed in young turkeys (first few weeks old).

Abstract

The agriculture and allied sector provide livelihood for 58% of India’s population. A large population and rising disposable incomes are key growth drivers. Growing external demand has increased its contribution to exports. The demand for processed food has also led to increasing requirements for agricultural inputs, hybrid seeds, machinery and fertilizers. Investment opportunities in storage infrastructure and agrochemicals also present immense opportunities. Organised retail has made India attractive for multinationals. The industry has a combination of multiple organisational forms, like associations, co-operatives, government (departmental) undertakings, foreign entities, trusts and companies, private and public. Unlisted companies encompass a vast range (private limited companies, family business, group-owned firms, and joint ventures are major actors.) The form and complexity of business ownership highlights Corporate Governance (CG) debate. Given this background, our objective is to understand the CG characteristics of unlisted agricultural and allied companies operating in India, the driving forces for improving CG practices, and the need for a policy framework in supporting good CG. We propose to examine the extent of control in the various types of entities and explore the mechanisms that contribute to the development of an efficient CG framework. Using data from the Prowess database, on 2,879 firms, from 2012-2013 to 2016-17, we analysed how CG problems are caused and how it can be alleviated. The results provide stakeholders and agri entrepreneurs with an overview of the policy and managerial issues that arise in the debate on CG of companies in this key sector. Finally, we conclude how the unlisted companies are likely to stimulate growth and development if CG mechanisms are enhanced.

Introduction to Corporate Governance

Corporate governance encompasses the framework of rules, practices, and processes that guide the direction, control, and management of a company. It involves the interactions among various stakeholders, including shareholders, management, the board of directors, employees, customers, suppliers, and the community. Successful corporate governance is vital for a company's long term success and sustainability. It helps establish a framework for responsible decision-making, accountability, and transparency, which enhances investor confidence and protects the interests of stakeholders.

Introduction to Corporate Level Strategy

Corporate level strategy pertains to the choices and initiatives undertaken by a company's senior management to define the overarching direction and breadth of the organization. It entails making decisions regarding which industries or markets to engage in and how to distribute resources among various business units.

Scope of Decision-Making: Corporate level strategy concentrates on the entirety of the organization rather than individual business units or functional areas. It involves strategic decisions related to diversification, vertical integration, mergers and acquisitions, strategic alliances & resource allocation.

Purpose: The primary goal of corporate level strategy is to create value for shareholders by achieving a sustainable competitive advantage and maximizing the overall performance and profitability of the organization. It involves assessing the external business environment, identifying opportunities and threats, and leveraging internal capabilities to gain a competitive edge.

Diversification: One key aspect of corporate level strategy is deciding whether to pursue a strategy of diversification. Diversification can be either related (expanding into industries or markets related to the company's current business) or unrelated (entering completely new industries). The decision to diversify involves assessing the potential benefits, risks, and synergies associated with entering new markets.

Organizations Indebtedness Towards Society

The organizations in the present world operate as an open system by having ongoing interactions with the society for their existence and effective functioning. There is an interdependence between corporations and societies and as such, company’s activities have a direct impact on the communities with which they work.  As a part of such interactions, the organizations use valuable resources of the society, for producing goods and services, which are exchanged with the customers for a profit margin. Due to these exchanges, the business organizations take away the profits reflecting the debt they owe to the society and the world.

Abstract
Corporate Social Responsibility is one of emerging term now-a-days in the corporate arena. Sustainable growth is possible only when all sphere of the society is self sustained. Since the early 1990’s, corporate responsibility issues including social obligation of corporations have attained prominence in political and business debates. This is mainly in response to corporate scandals but also due to realisation that development centred only on economic growth paradigms is unsustainable and therefore there is a need for more pro-active roles by states, companies and communities in a development process aimed at balancing economic growth with environmental sustainability and social cohesion. Some people think sustainability is all about environmental issues. Others see it in terms of the bottom line. And then, of course there are the people who use the term synonymously with corporate social responsibility. CSR and Sustainable development are two different concepts having different traits. Corporate social responsibility (CSR) promotes a vision of business accountability to a wide range of stakeholders who are directly and indirectly dealing with corporate entities, besides shareholders and investors. Therefore, there is a need for evaluating the working of CSR on social sustainability. Besides, this paper try to identify the key areas of concern are environmental protection and the wellbeing of employees, the community and civil society in general, both now and in the future (by international institute for Sustainable Development),various issues and challenges faced by corporate bodies.

Seed yield is the foremost important attribute and is the end product of many complex component characters which singly or jointly influence the seed yield. Seed yield does not possess gene per se as such. It is the interaction phenotype of the harmonious understanding, mutual adjustment and manifestation of its component characters. As most of the component characters are polygenic, they are greatly influenced by the environment. So, the selection of genotypes based on yield as such is not likely to be effective. The information on the strength and direction of association of the component characters with seed yield and also among themselves will be very useful in formulating an effective breeding programme for seed yield improvement in a given time. Correlation studies provide about the magnitude and direction of genetic association between the seed yield and its component characters.

Correlation coefficients provide the measure of association between two characters. But, they do not provide the causal basis of such association. Further, correlation between seed yield and its component characters are often misleading due to the inter-relationship that exist among the component characters and the operation of size symmetry and component compensation that thwart the breeding progress. The direct contribution of each component to the seed yield and the indirect effects it has through its association with other components cannot be differentiated from mere correlation studies. This is how, it became necessary for a plant breeder to have information on the direct and indirect effects of these components on the seed yield to identify the key characters for improvement.

Correlation and regression are essential statistical tools for examining relationships among climate variables and agricultural responses. Correlation measures the strength and direction of association, while regression develops mathematical relationships for explanation and prediction. This chapter discusses their application to rainfall, temperature, humidity, evapotranspiration, crop growth and yield. It explains simple and multiple relationships, assumptions, interpretation, model fitting, predictive power and potential errors. These methods support crop-weather modelling, yield forecasting, climate-impact assessment and agricultural decision-making. By identifying how climatic variables influence biological and production processes, correlation and regression analysis help researchers generate reliable predictions and design more resilient and productive agricultural management strategies under changing environmental conditions.

10.1 Introduction

In previous lessons we have been dealing with problems connected with variation occurring in any one variable, say the income of a person, the height or weight of an individual, the wage of a worker in a factory, the sales of a shop, demand of a commodity etc.  The values of a single variable were obtained from every member of the population and the different characteristics were described by certain descriptive constants after grouping into frequency distribution.  In this lesson, we shall be dealing with problems wherein there are paired variables and every individual or member of the population exhibits two values, one for each of the variables under consideration.  Example of the two variables which are to be studies together are (i) the income and expenditure of a household, (ii) the price and demand or supply of a commodity, (iii) the height and weight of a student, and (iv) the grain yield and straw yield of paddy, (v) Height of father and height of son, etc.

Concept of correlation was given by Karl Pearson in 1890.

The study of strength and nature of relationship between two variables is called correlation. Two variables are said to be correlated, if the change in one variable affects the other variable. Correlation is also known as covariation.

Bivariate Distribution: Distribution involving two variables is known as bivariate distribution. For example, Height and weight of persons, yield and production of a crop in.

On basis of type of association between variables, correlation is of two types:

If x and y are related, we can study degree of relationship between them which is called correlation.

Y = f(x)=a+bx where y is a dependent variable and x is an independent variable. If x is known, y can be known.

X = f(y). Here x is a dependent variable and y is an independent variable or auxiliary variable.

Correlation coefficient

We measure the degree of association between two variables by calculating Pearson’s product moment correlation coefficient, usually just called the correlation coefficient.

It was developed by Karl Pearson from a related idea introduced by Francis Galton in the 1880s, and for which the mathematical formula was derived and published by Auguste Bravais in 1844. In 1896, Karl Pearson published his first rigorous treatment of correlation and regression in the Philosophical Transactions of the Royal Society of London.

The linear correlation coefficient (sometimes called Pearson's Correlation Coefficient), commonly denoted, is a measure of the strength of the linear relationship between two variables.

• We say that we have perfect correlation if all the points lie on the line; in this case, the value of the correlation coefficient takes one of its extreme values, either +1 or −1 (Figure 7a and 7b).

• We have positive correlation if the sign of the correlation coefficient is positive; then there is a direct relationship between the two variables so that as one variable increases in value, the other variable increases (Figure 7a) or there is a tendency for it to do so.

• We have negative correlation if the sign of the correlation coefficient is negative; then there is an inverse relationship between the two variables so that as one variable increases in value, the other variable decreases (Figure 7b) or there is a tendency for it to do so.

• We have no linear association (i.e. the variables are uncorrelated) if the correlation coefficient is zero; then there is a random scatter of points with no indication of a linear relation between the variables (Figure 7c.). Note that a non-linear relationship between the variables can also give a correlation coefficient of zero.

6.1 Correlation

The term correlation indicates the relationship between two such variables in which with changes in the values of one variable, the values of the other variable also change. In short, the tendency of simultaneous variation between two variables is called correlation.

Correlation

In a statistical analysis, when two variables are studied simultaneously, then it is called “Bivariate study’. Now, in a bivariate study, if it is found that change in the values of one variable then in sympathy with the change in the values of other variable, then these two variables are said to be associated and the association is termed as correlation. In other words, the term correlation indicates the relationship between two such variables in which with changes in the values of one variable, the values of the other variable also change. In short, the tendency of simultaneous variation between two variables is called correlation.

Types of Correlation

Correlation can be classified by-

• Positive and Negative correlation: The correlation may be classified according to the direction of change in the two variables. In this regard, correlation may be either positive or negative. Positive correlation refers to the movement of variables in the same direction. Negative correlation efers to the movement of variables in the opposite direction.

• Simple, Partial and Multiple correlations: When only two variables are studied it is a problem of simple correlation. When three or more variables are studied simultaneously, it is a problem of multiple correlations. On the other hand, in partial correlation we recognize more than two variables, but consider only two variables to be influencing each other the effect of other influencing variables being kept constant.

• Linear and non-linear correlations: In linear correlation for every unit change in the values of one variable, there is constant change in the value of other variable. The perfect positive and negative correlations are also linear correlations. In non-linear correlation the change in the values of one variable does not have a constant ratio to the change in the other variable.

Germination between M₁ and M₂ had a non-significant relationship in germination between M₁ and its corresponding M₁ progenies in M₂ (Table 27). Probably the reduction in the germination in M₁ might be very highly and significantly influenced by the conditions prevailing in germination and physiological disturbances that would be strictly associated in M₁ alone. Whereas the survival of the seedlings between M₁ and M₂ showed a strong positive relationship. The differential behaviour of the mutagens affecting germination and survival was also evident as discussed elsewhere.

However, seedling height on 10^th day sorted a differential relationship between M₁ and M₂. It was negative and non-significant. A non-significant relationship was exhibited for seedling height on 30^th day between M₁ and M₂ in all the doses. It may be interpreted that extent of physiological disturbances that occurred in the early stages and the extend of recovery mechanism that operated subsequently may be the major cause for this; not withstanding the haplontic and diplontic selection that operated in the biological system. Further the type of physiological disturbances happened, whether the enzyme systems affected or a substantial amount of toxic substances produced besides the genetical causes have not been studied in the present investigation. Moreover if the reduced growth was due to mitotic arrest in the meristamatic regions of the roots of M₁ plants, the disorder may not be completely inherited to its offspring’s (M₂ plants), in that sense selection at the cell level damages might occur in the M₁ itself and the selection for a cell with normal mitosis and extent of chromosomal damages that will be tolerated alone might be selected and hence the effect in M₁ need not be always related with M₂; it also depends upon the potency of the cell to recover from the mutagen induced damages. The ability of the cell in general, should be more in M₁ than in M₂ recover the disorder after several cell generations.

This chapter begins with an introductory overview, providing a basic understanding of correlation and its types. It guides readers through in-depth analysis using R, demonstrating how to apply these concepts practically.

Correlation

Correlation analysis is a statistical method used to evaluate the strength and direction of the relationship between two variables. This analysis is vital in identifying and understanding how variables are interconnected within a dataset, providing insights into their mutual influences.

Key aspects of correlation include

• Range of the correlation coefficientThe value of the correlation coefficient (“r”) falls within the range of −1 to +1.

• Independence from origin and scaleThe correlation coefficient (“r”) remains consistent regardless of any changes in the origin or scale of the variables.

• Unit independenceThe correlation coefficient is independent of the units of measurement

of the variables.

• Relationship with independenceWhile uncorrelated variables are generally independent, the reverse is not necessarily true; independent variables will always be uncorrelated.

6.1 Learning Objectives
After going through this chapter, you will be able to:
i) Understand the meaning, significance, and properties of correlation;
ii) Know the various types of correlation;
iii) Comprehend the computation process of various types of correlation;
iv) Appreciate the limitations of correlation.

6.2 Introduction

Correlation is a fundamental statistical concept that measures the degree of association or relationship between two or more variables. Correlation helps us to understand how changes in one variable correspond to changes in another. Correlation values range from -1 to 1; where -1 indicates a perfect negative correlation, +1 represents a perfect positive correlation and 0 suggests no linear correlation between the variables. The sign and magnitude of the correlation coefficient provide information about the strength and direction of the relationship between the variables. Correlation analysis is a multipurpose tool with various applications, such as identifying the strength of relationships between stock prices and market indices, assessing the impact of education on income levels, or determining the association between weather conditions and crop yields. Understanding correlation is essential for making informed decisions, drawing conclusions from data, and developing predictive models in numerous fields of study and practice.

In genetic studies it is necessary to distinguish two causes of correlation; is chiefly pleiotropy, though linkage is a cause of transient correlation, particularly in populations derived from crosses between divergent strains. Pleiotropy is simply the property of a gene whereby it affects two or more characters, so that if the gene is segregating it causes simultaneous variation in the characters as affects. For example, genes that increase growth rate increase both stature and weight, so that they tend to cause correlation between these two characters. The degree of correlation arising from pleiotropy expresses the extent to which two characters are influenced by the same genes. But the correlation resulting from pleiotropy is the overall, or net, effect of all the segregating genes that affect both characters. Some genes may increase both characters, while others increase one and reduce the other the former tend to cause a positive correlation the latter a negative one. So pleiotropy does not necessarily cause a detectable correlation. The correlation resulting from environmental causes is the overall effect of all the environmental factors that vary; some may tend to cause a positive correlation, others a negative one.