eChapters

Introduction

Regression analysis is one of the most widely used techniques for studying relationships involving multiple variables for analysing data by expressing a relationship between a variable of interest (the response) and a set of related predictor variables. The regression models include linear and non linear approaches assuming appropriate functional forms. A good account on regression analysis and related topics can be found in Draper and Smith (1998), Montgomery et al. (2001), Chatterjee and Hadi (2006) etc. In this write-up, regression model fitting, some of the detection techniques which are useful in detecting the problem of multicollinearity between the so-called ‘independent variables’ and also outlier detection in data are discussed. Linear regression with qualitative regressor variables is also discussed. Moreover, variable selection procedures, goodness of fit measures for model adequacy and validation are also discussed. In addition, non-linear regressions viz., logistic (both binary and multinomial) when the response variable is qualitative are also covered.

Multiple Linear Regression Modeling

Let the response variable (variable to be forecasted) be denoted by Y and the set of predictor variables, by X1 , X2 , …, Xp , where p denotes the number of predictor variables. The true relationship between Y and (X1 , X2 ,…,Xp ) can be approximated by a multiple linear regression model given by

Setting

The basic difference which dovetails most of the animal breeding experiments is the unbalancedness of the data collated with respect to factorial classes thereby making non-orthogonal nature of the effects involved ubiquitous. Henderson, C.R. (1953), through a series of seminal works dealt with these types of data and came out with three distinct methods to estimate the variance components emanating out of such non-orthogonal animal experiment data. Before discussing those methods, let us delve a bit on the challenges in estimating the variance components in animal breeding data. There are two major reasons, first, several methods of estimation are available (most of which are reduced to the analysis of variance method for balanced data), but not one of them has yet been clearly established as superior to the others. Second, all the methods involve relatively cumbersome algebra; discussion of unbalanced data can therefore easily deteriorate into a welter of symbols, a situation which functions as the anti-appetizer to applied researchers.

A safe starting point towards understanding the nuances of dealing with unbalanced data could be the paper by Henderson (1953) which is quite seminal in nature. The methods described have been often referred to as the gold standard in the area of estimating variance components from unbalanced data for many years to come. Of the three, method 1 is simply an analogue of the analysis of variance method used with balanced data and method 2 is used to correct a deficiency of method 1 that arises out of mixed models and the last one is quite different and involves the method of fitting constants so often used in fixed effects models. Let us see these methods in a bit more detail in the ensuing passages.

Introduction

The term ‘Fish Marketing’ is composed of two broad meaning term ‘Fish’ and ‘Marketing’. Before discussing the methodological issues of fish marketing data analysis, we must understand the broader meaning of it as both words are being used frequently with a précised meaning. Google dictionary explains fish as ‘A limbless cold-blooded vertebrate animal with gills and fins living wholly in water’. But ‘fish’ as a component of fish marketing or social science study, means a product and a variety of product, which has different time, space and form utilities for consumers.

The word ‘marketing’ is not merely buying and selling of fish, rather it connotes a series of activities involved in moving goods or services from point of production to the point of consumption. So ‘fish marketing’ is defined as physical and institutional set up to perform all activities involved in the flow of fish, fish products and services from the point of initial fish production until they are in the hands of ultimate customers.

It includes assembling, handling, storage, transport, processing, wholesaling, retailing and export of fish and fishery products as well as accompanying supporting services such as market information, establishment of grades and standards, community trade, financing and price risk management and the institutions involved in performing the above functions.

It is desired to have an efficient and well planned distribution networkso that fish and fishery products move from the place of landing or production to the place of consumption at cheapest cost with quality –intact, ensuring the maximum price realization to the farmers or fish producers. It is really challenging to the public agencies involved to meet the requirements of all three major players, viz., fishermen or fish farmers, consumers and traders.

It includes all activities involved in creation of time, place, form and possession utility. Philip Kotler explained marketing as the science and art of exploring, creating, and delivering value to satisfy the needs of a target market at a profit. ‘Fish marketing’ is different from marketing of other goods and services and even differs a bit from marketing of variousagricultural goods and services.

Introduction

Resources must be managed effectively to achieve maximum sustainable agricultural production from limited natural resources. Many of the resources that agriculture depends on (such as water and land) are finite and need to be well managed for long term sustainability. In most cases, this means avoiding adverse effects on the society and environment. With the development of agricultural sectors, the utilization patterns of natural resources in agriculture are important concern for policy makers in present climate change scenario. Unlike other production activities, agricultural activities also utilize factor resources to produce several marketable products or services. The way the natural resources are utilized in agriculture and its concern for society and environment, plays a vital role in economic viability and ecological sustainability of agriculture.

There are several conventional methodologies to estimate the level of profit and efficiency of production system at enterprise level. Only marketable factors are considered in conventional methods keeping the non-marketable component of production system away. Non-marketable component of a production system includes various social and environmental dimensions of it. Nammalwar (1997) has explained the concept of environmentally sound and sustainable enterprises, which include the physical resources, ecological resources, human use value, quality of life values with either way impact of agriculture and environment. In recent years, non-marketable dimensions of agricultural activities have been given importance because of its concern for the environment and society. This chapter provides a methodology that incorporates non-marketable component of agricultural enterprise besides its marketable component. The Resource Cost Ratio (RCR) approach is a modified methodology developed by Debnath et al. (2010) from Domestic Resource Cost Ratio (DRCR) that Morris (1990) has applied at macro-economic level to determine the comparative advantage between countries.

Introduction

This chapter is intended to throw light on some statistical and econometric methods used for analysing time series data with specific emphasis on basic concepts related to time series analysis, co-integration techniques as well as forecasting techniques such as ARIMA model. Time series analysis is often helpful in revealing the broad patterns associated with longitudinal data, which in turn would aid in important economic and policy decisions. In fisheries sector too, economic variables such as market demand and supply, prices, etc. are of prime importance and often come in longitudinal format. There are a number of alternative methodologies available in the time series literature to determine relevant parameters pertaining to longitudinal data and the ensuing sections are set apart to delve deeper into this realm.

Some Key Concepts

Stationary Stochastic Process

A random or stochastic process is a collection of random variables ordered in time. A stochastic process is said to be stationary if its mean and variance are constant over time and the value of covariance between two time periods depend only on the distance or the gap between the two time periods and not the actual time at which the covariance is computed. In the time series literature, such a stochastic process is known as a weekly stationary or second order stationarity or covariance stationarity.

There has been a surge of data in every field of research due to advancements in information technology. Data collection and storage has become much easier and cheaper in recent years. This voluminous data needs to be systematically stored for quick retrieval, process and to find useful patterns. Data mining techniques help in deriving useful patterns from data. These techniques which are also called as machine learning techniques majorly categorized as classification, clustering and association rule mining methods. Classification is a supervised learning method which needs labeled dataset for model building and prediction purposes. Clustering and association rule mining methods do not require labeled dataset and hence they are referred as unsupervised learning methods.

Clustering aims at grouping the objects based on a set of measured variables where similar objects are assigned to one group. For example, let us say, if we have collected information on 50 fish pond parameters (50 variables) and we wish to find the groups in terms of pond management (like well managed to poorly managed), with cluster analysis, we can group the ponds based on pond variables. In the process of clustering, each pond is assigned to one of the groups made. The question is how many natural clusters present in the data as in many cases we would not be getting perfect clusters of disjoint sets. We can achieve better accuracies in cluster formation only by trial and error. Good clustering criteria are to see that there is minimum with-in cluster variations and maximum between cluster variations.

Cluster analysis has several applications in fisheries research. It could be used to group fish stocks from different regions, categorise ponds based on their technical efficiencies, identify the farm clusters with high disease risk, group farmers based on their preferences, agro zoning based on climatic variables and culture practices. Cluster analysis is also applied in fish bioinformatics to group the similar sequences.

Introduction

Importance of basic statistics in everyday life cannot be stressed enough. Inclusion of basic statistics in high school curriculum is uncommon but proves highly beneficial to supplement certain careers in future. This field may be the most widely used of all and most people don’t even know they are using it. Many of today’s brightest business minds use this study to create charts and graph that allow them to stress a certain aspect of the business they are dealing with. Market research agencies, News rooms, marketing divisions of large corporations, NGOs, accounting firms etc. are full of people who specialize in basic statistics.

Statistics is concerned with the describing, interpreting and analyzing data. It is, therefore, an essential element in any improvement process. Statistics is often categorized into descriptive and inferential statistics. It uses analytical methods which provide the math to model and predict variation. It uses graphical methods to help making numbers visible for communication purposes.

Descriptive Statistics

• Descriptive statistics summarizes or describes characteristics of a data set.

• They help to make sense of large numbers of individual responses, to communicate the essence of those responses to others

• Helps exploring and making conclusions about the data in order to make rational decisions.

• Includes calculating things such as the average of the data, its spread and the shape it produces.

• Are displayed as tables, charts, percentages, frequency, distributions and reported as measures of central tendency

• Determine if the sample is normally distributed (bell curve) as most statistical tests require the sample to have normal distribution

• Determine if the sample can be compared to the larger population

Difference between Descriptive and Inferential Statistics

Nowadays, statistics is playing a major role in the field of research; that helps in the collection, analysis and presentation of data in a measurable form. It is quite hard to identify, whether the research relies on descriptive statistics or inferential statistics, as people usually, lacks knowledge about these two branches of statistics. As the name suggests, descriptive statistics is one which describes the population. On the other end, Inferential statistics is used to make the generalisation about the population based on the samples.

Experimental Designs

Experimental Designs are used widely to compare more than 2 groups, commonly called as treatments. It is very common in agriculture to conduct several experiments, mostly in fields to test various varieties, different levels of inputs, etc. When one is interested in crop modelling, it is necessary to identify the values of several constants, such as input efficiency, growth periods, growth rates, etc. Instead of guessing these values, it would be more appropriate to conduct experiments to derive these values empirically, which can improve the modelling efforts. The principles of experimentation were given by Sir R.A. Fisher during 1920’s, which are still relevant and found in every scientific research namely, (i) Randomization, (ii) Replication and (iii) Local Control. The following sections introduce some of the commonly used terms in Experimental Designs:

Factor: Variables which are set/considered by the experimenters in any experiment. Examples of factors are: Nitrogen, Varieties etc. Level: The various values of a particular factor considered in the experiment. Examples of levels are: Rates of nitrogen applied, different varieties considered in the experiments.

Treatment: The levels of one or more variables (Factors) set/considered by the experimenters.

Note that in many experiments, the number of variables (i.e. factors) considered may be one only.

Need of Analytics in Fisheries Management

New technologies are introduced for industrial vessels every day: Fuel and gear sensors, cameras, electronic catch reports etc. There is a global push to track small-scale fishing vessel. In the coming years, fisheries monitoring centers will have to adjust from tracking a few hundred vessels to tens of thousands of vessels, meaning huge quantity of data. To make sense of all this information, it must be properly analysed. The future of fisheries depends on big-data analytics

This may help in many ways. Fishing trips could be tailored to meet quotas in the shortest time reducing fuel consumption and crew costs. Real-time catch data could help administrations close a fishing zone once quota is reached. Fisheries administrations can monitor catch efforts over an entire region and decide which zones to close, improving management of stocks and fishing licenses. Fighting the challenge of Illegal and Unreported fishing contributing to over-exploitation of fish stocks. Use of disruptive technologies in fisheries and aquaculture like block chains, sensors and automatic identification systems (AIS) has the potential to change the processes profitability and sustainability of the sector

Globally used popular Data Science Software

• MS-EXCEL

• SPSS (IBM)

• STATA • MINITAB

• SAS

• R

• PYTHON

Introduction

Indian fisheries and aquaculture is an important sector of food production providing nutritional security, besides livelihood support and gainful employment to more than 14 million people, and contributing to agricultural exports. With diverse resources ranging from deep seas to lakes in the mountains and more than 10% of the global biodiversity in terms of fish and shellfish species, the country has shown continuous and sustained increments in fish production since independence. The sector constitutes about 6.3 percent of the global fish production and contributes to 0.91 percent of the GDP and 5.23 percent of the agricultural GDP. The total fish production of 13.34 million metric tonnes presently has nearly 74 percent contribution from the inland sector and rest by the marine sector. Paradigm shifts in terms of increasing contributions from inland sector and further from aquaculture have been significant over the years. With high growth rates, the different facets, viz., marine fisheries, coastal aquaculture, inland fisheries, freshwater aquaculture, and coldwater fisheries are contributing to the food basket, health, economy, exports, employment and tourism of the country. More than 50 different types of fish and shellfish products are being exported to 75 countries around the world. Fish and fishery products have presently emerged as the largest group in agricultural exports from India, with 13.77 lakh tonnes in terms of quantity and ` 45,106.89 crore in value. This accounts for around 10% of the total exports and nearly 20% of the agricultural exports, and contribute to about 0.91% of the GDP and 5.23% to the Agricultural GDP of the country.

The Pradhan Mantri Matsya Sampada Yojana (PMMSY) is an initiative launched by the Government of India to establish a comprehensive framework and reduce infrastructural gaps in the fisheries sector. The scheme was announced by the Finance Minister, Smt. Nirmala Sitharaman during her speech in the Parliament of India while presenting the Union budget for 2019–20 on 5 July 2019. The government intends to place India in the first place in Fish production and processing by implementing Blue Revolution. This scheme is in line with government’s aim to double the farmers’ income by 2022–23.The policy envisages integrating all the fishermen with agricultural farmers and providing all the facilities available through various farmer welfare schemes to the fishermen. A new dedicated Department of Fisheries was constituted in a newly carved out Ministry of Fisheries, Animal Husbandry and Dairying to implement this and other policy initiatives of the government.

Introduction

The term analytics and data analytics are used inter-changeably. When referring to analytics, it means “analysis of data” or “what can be learnt from an analysis of data”. In an industry like fishing industry, the researchers have been slow to adopt the tools used to portray accurate representations of data. For example, a dashboard with historical data from the past ten or fifteen years displayed on a digital map can enable the researchers to decide the places where fishing can be good. This map can help in locating the points fished during specific seasons. This will enable the researchers to filter the data on various parameters like area, year, season etc. or on any other specific parameter which can give valuable information. Similarly, this type of dashboard can affect the decision making process while fishing, like one can find the monetary value associated with each trip during a specific season or compare the values associated with two different trips to gauge the cost-benefits.

Data Analytics helps in taking most informed decisions in the fishing industry if one of the goals is to increase income while ensuring sustainability. It helps in deciding whether to reduce overhead costs or to increase expenditure to avoid overhead costs in future.

Fisheries sector undergoes continuous changes with introduction of newer technologies evolved through research and development institutions. Statistics per se deals with generation of data, data management, data analysis and information generation from data. There are varied disciplines of fisheries viz., Aquaculture, Fisheries Resource Management, Fish Genetics, Fish Biotechnology, Aquatic Health, Nutrition, Environment, Fish Physiology, and Post-harvest Technology, wherein research on practical aspects is undertaken which affect production and sustainability.

India is primarily an agriculture based economy, which implies, agriculture dominates the nation’s economic landscape. In the course of socioeconomic growth, agriculture is crucial. Around 58 per cent of the population in India relies only on agriculture for their livelihood. India is enriched with various agricultural and horticultural crops with diversified agro-climatic conditions and different soil types. India is one of the leading producing countries of cereals, pulses, oilseeds, fruits, vegetables etc. India is also have a prominent scope in allied sectors of agriculture includes livestock, poultry, forestry, sericulture etc. Among such crops, Moringa is the selected crop for this research study. Moringa (Moringa oleifera), which is popularly known as ‘Drumstick’ belongs to the family Moringaceae. Moringa is basically a native crop of India. This crop has its own therapeutic properties with greater nutritional content. Especially in South Indian cuisine, moringa pod is a highly regarded vegetable because of its distinctive flavour and fragrance.

This chapter analytically represents the results of the empirical research study with the appropriate discussion on the basis of analysis.

6.1. Results of data collected from moringa growers of Tamil Nadu

6.1.1. Visualisation of descriptive statistics

This Table-6.1 presents 18 independent variables (x1-x18 ) and 5 dependent variables (y1-y5 ) in terms of their descriptive statistics viz. Minimum, Maximum, Range, Mean, Standard Deviation (S.D.), Variance and Co-efficient of Variation (C.V.).

of moringa growers, the maximum years of experience is 44 years and the minimum years of experience is 3 years. The Range of this variable x1 was found to be 41. The mean value of experience was found to be 16.033 years, with the Standard Deviation of 8.324 and Variance of 69.289 for the total distribution taken for the study.

5.1. Research methodology

Research methodology is a thorough investigation plan and the manual for conducting the research. This chapter’s examination of methodology aims to help readers comprehend the ideas, strategies, and tactics used to plan a study, gather data, analyse it, and interpret it in order to reveal reality and develop theories. This chapter has been classified under various sub headings for clear understanding:

(i) Locale of research.

 (ii) Pilot study.

 (iii) Methods of sampling.

 (iv) Variables and their empirical measurement.

 (v) Preparation of the interview schedule.

 (vi) Tools and techniques of data collection.

 (vii) Statistical analysis and interpretation of data.

Introduction

Research setting is basically a functional social ecology along with research objectives and condition wherein all possible socio-economic and communication processes are generated, and again gets regenerated. The researcher seeks to throw a spotlight on the implications and behavioral patterns of people who reside in the subject region and who are usual of the greater community. In this chapter, an in-depth review of the socio-demographic background of the local population in a rural setting has been provided.

4.1. Area of the study

The present research study was taken up in two states, Tamil Nadu and West Bengal.

3.1. Entrepreneurship and its allied concepts

3.1.1. Concept of entrepreneur

The word “entrepreneur” has its roots in the french verb “entreprendre,” which means “to undertake.” It means “to start a business” in a business environment. Entrepreneurs are self-employed individuals who not only create jobs for themselves but also generate jobs for many others. An entrepreneur is someone who can identify and seize opportunities to transform inventions or technologies into new products. They can also see an invention’s commercial potential and put together the funds, talent, and other resources needed to transform an idea into a product that can be distributed.

2.1. Concept of review of literature

Research is a continuous process. For any scientific investigation, previous f indings provide basis to the research. It also helps to know the previous research work done in that area and acts as a torch for the new researchers. The review of literature is one of the important aspects in the research process. It helps the researcher to keep his work going in right and appropriate direction. To point out prior research in the area of interest and how our findings connect to past studies, a review of the literature is conducted. The proposed study is new in its subject treatment. However, an attempt is made here to put together some of the closely related research findings in the area.

India is primarily an agriculture based economy, which implies, agriculture dominates the nation’s economic landscape. In the course of socioeconomic growth, agriculture is crucial. Around 58 per cent of the population in India rely only on agriculture for their livelihood. In India, agriculture acts as an important source of living for about 60 per cent of the population (India Brand Equity Foundation, 2021) and almost 70 per cent of rural households depend on farming. The farming sector in India contributes significantly to the economy, making up about 29 per cent of Gross Domestic Product (GDP) (Balkrishna et al. 2021).

15.1 Sample Objective Question

1. Which one is related to deposition of Micro-organism?
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest Treaty
   4. Patent Cooperation Treaty.
2. WTO was officially commenced in the year...........
   1. 1970
   2. 2003
   3. 2005
   4. 1995
3. Which one is related to protection of Trademarks
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest treaty
   4. None of these
4. One of the four patent office is...........
   1. Patna
   2. Banglore
   3. Varanasi
   4. Mumbai

Traditional knowledge is a type of Intellectual property. It is also known as indigenous knowledge generated from the local communities and tradition of regional technology areas. This knowledge is orally passed over generation from person to person. According to world intellectual property organization, Traditional knowledge is an indigenous, cultural and intellectual property. The biodiversity of a country, the uses and applications of biological resources and prevalent practices are examples of traditional knowledge associated with life science. TK has the tremendous potential of being transformed into commercial opportunity by the development of products and processes. Hence, a share of benefits must accrue to creators and holders of Traditional Knowledge.