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Fishing

Fishing Subsidies Nowadays Overview

Fishing Subsidies Nowadays Overview.
Fishing subsidies continue to increase, and as of 2009, the figure had reached US$35 billion. The number represents about thirty per cent to forty per cent of the land values that are produced by marine fisheries all over the world (Sumaila, 2015). The existing literature shows that there are three broad groups of subsidies. First, there is capacity-enhancing subsidies. This one involves fuel and construction which promote overexploitation of species of fish (Sumaila, 2015).
Then there are the additional subsidies that are referred to as ambiguous, they include those relating to rural fisher development and vessel buy-back programs. Scholars argue that these subsidies can either undermine or promote the sustainability and availability of fish depending on how they will be rolled out and designed. Lastly, there are subsidies intended for research and management (Sumaila, 2015). These types are regarded as satisfactory subsidies since they confer positive impacts on people’s inherent efforts to manage the resources in the fishing industry for the good of all generations.
Schrank & Wijkström (2003) use different criteria when categorizing fishery subsidies, which differs from the one used earlier. The first category involves direct payments made by the government to the fishing industry, which include but is not limited to equity infusions, compensation for closed seasons, vessel decommissioning payments as well as price support programs.

Second, there are deferrals and tax waivers which include exemptions of tax fuels used for fishing. Schrank, & Wijkström, (2003) believe that if there is the exception of sales tax linked to inputs, especially those used in the fishing industry, such exceptions offer general support for the industry. Government insurance, loan and guarantees are provided by the government to make fishing firms and fishers able to operate when it would otherwise not be possible to do so.
In addition, the government offer fishers insurance since other private insurers consider them highly uncertain as the industry has considerable risk.
Lastly, they point out that the implicit payments to, or charges made against the fishing industry as the other categorization that FAO recognizes concerning fishing subsidies (Pramod et al., 2014). The subsidies groups under this category involve programs that fail to transfer, waive or defer payments that are often generated by the industry and geared towards the government. Such programs include but are not limited
Aquaculture, EEZ, Restriction for implementation to those aimed at reducing prices the industry pays the government for products that fail to meet the market prices, as well as programs that may not include the government payment at all.
Schrank & Wijkström (2003) argue that FAO considers all these programs good for the fishing industry. According to scholars, FAO acknowledges that there are bad subsidies. In addition, the organization also notes a number of these regulations which include but are not limited to fisheries regulations require excluder tool and other safety and environmental regulations. The problem with fishing subsidiaries is that they are not good for the industry for the long term.
Patrick, & Benaka, (2013) argue that the unhindered effect of subventions on fish as part of natural resources is dependent on the overall health exhibited by the stock as well as the fortitude of the management put involved. In particular, fisheries and their corresponding management are rarely operative, and there is overwhelming evidence to show that subsidies by themselves alone can compromise efforts put in place to manage stocks sustainability.
Balsas (2019) has shown that such subsidies not only disorganize the market but also often negatively affect fishers that receive fairly fewer subsidiaries. It is worth noting that most subsidiaries tend to be taken up large-scale corporate size fishers belonging to developed nations as opposed to those in developing countries.
Concerning commercial fishing, the enterprises appear to be driven by profit, which implies that an increase in profit increases fishing. The problem, according to Baden, & Bianconi, is that this stimulation of profit increases a certain kind of race for fish in the industry. This harms the population of fish which need to be maintained for future generations.
Even though certain players within the fishing industry have blamed regulations put in place by the World Trade Organization for the current overfishing taking place in Massachusetts, FAO does not subscribe to this idea.

Fishing Subsidies Nowadays Overview

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Contribution of Fishing Industry Towards Poverty Reduction in Zanzibar

Contribution of Fishing Industry Towards Poverty Reduction in Zanzibar.
THE UNIVERSIRY OF DODOMA COLLEGE OF HUMANITIES AND SOCIAL SCIENCES SCHOOL OF ECONOMICS AND BUSINESS STUDIES DEPERTMENT OF ECONOMICS AND STATISTICS RESEARCH – PROPOSAL. TOPIC: The contribution of fishing industry towards poverty reduction in Zanzibar. SUPERVISOR: CANDIDATE: MR. BONGOLE, A J MUSSA, HANIFU T/UDOM/2010/03536 Table of Contents THE UNIVERSIRY OF DODOMA1 COLLEGE OF HUMANITIES AND SOCIAL SCIENCES1 SCHOOL OF ECONOMICS AND BUSINESS STUDIES1
DEPERTMENT OF ECONOMICS AND STATISTICS1 LIST OF ABBREVIATION3 CHAPTER ONE4 OVERVIEW OF THE STUDY4 1. 0 Introduction4 1. 1 Background Information to the problem4 1. 2Statement of the problem. 5 1. 3. Significant of the study5 1. 4 Scope of the study5 1. 5Objective of the research5 1. 5. 1General objectives. 5 1. 5. 2Specific objectives5 1. 6 . Hypothesis of the study6 CHAPTER TWO7 LITERATURE REVIEW. 7 2. 0. INTRODUCTION7 2. 1 Definition of fishing7 2. 1 Background of fishing Industry7 2. 2 Fishing in Zanzibar’s economy7 2. 3 POVERTY REDUCTION8 . 4 Definition of poverty8 2. 4. 0: Growth and poverty8 CHAPTER THREE9 3. 0: METHODOLOGY9 3. 1 AREA OF THE STUDY10 3. 2 TARGETED POPULLATION10 3. 3 PURPOSE OF THE STUDY AND TYPE OF INVESTIGATION10 3. 4 DATA COLLECTION. 10 3. 5 SAMPLING DESIGN10 3. 6 QUESTIONNAIRE SURVEY11 3. 7 THE INTERVIEW SURVEY11 3. 8 DATA ANALYSIS11 3. 9 CONTRIBUTION OF THE STUDY11 MODEL OF THE STUDY11 BIBLIOGRAPHY12 QUESTIONNARES13 LIST OF ABBREVIATION BOT – Bank of Tanzania DD – Demand FDZ -Fisheries Department of Zanzibar
GDP – Gross Domestic Product GOZ – Government of Zanzibar MOFEA – Ministry of Finance and Economic Affairs SMEs – Small and Medium Enterprises TZS – Tanzania Shillings UK – United Kingdom URT – United Republic of Tanzania USD – United States Dollars ZIPA – Zanzibar Investment Promotion Authority ZIP -Zanzibar Investment Policy ZNZ – Zanzibar ZPRP – Zanzibar Poverty Reduction Plan CHAPTER ONE OVERVIEW OF THE STUDY 1. 0 Introduction

This chapter covers the contextual background of the problem stating clearly how the problem come about/historical development and what is being done so far on literature review , stating clearly the statement of the problem, general and specific research objectives, general and specific research questions. 1. 1 Background Information to the problem Zanzibar’s fishing is almost entirely artisanal and is conducted in the shallow waters along the coast. The entire fishing grounds are about 4,000 square kilometers for Unguja and 2,720 square kilometers for Pemba.
Much of this area has coral reefs and a variety of flora and fauna making the region ideal for fishing. Indeed, there is an enormous potential for increased production of marine products, through offshore and deep-sea fishing including processing, for both domestic and export markets(ZIP). The Zanzibar Poverty Reduction Plan (ZPRP 2002) stipulates that growth in the agricultural sector is crucial due to its pro-found positive impact on poverty reduction. Based on this back drop, once growth in agriculture is stimulated, most poor people in this sector will benefit culminating into poverty reduction.
The fishing sub-sector has a relatively lower contribution in export compared to other exports such as cloves, manufactured goods and other exports. Statistics show that from 2000 to 2004 exports of fish amounted to USD 0. 53million accounting for 0. 7%of total exports amounting to USD 67. 5 million. (ZPRP 2002) However, the market potential is yet to be sufficiently exploited because of a fish catches , not withstanding the fact that Zanzibar is surrounded by sea. Generally, fishing activities in Zanzibar are concentrated on onshore.
According to the Agricultural Policy(2000),the main reason for shallow sea fishing with low fish yield is lack of capital to purchase larger vessels to engage in deep sea fishing, indicating that fishing is not developed (some of fishermen do not use fishing vessels but use rudimentary tools for catching fish such as spears sticks, knives, small nets and bare hands). Fish stocks include small pelagic, coral reef fish, lobsters, octopus and large pelagic etc The fishing territorial area is made of about 4,000sq. kms for Unguja or 59. 5% and 2,720sq. kms for Pemba accounting for 40. 5% of total.
Statistics for fish catch indicate a fluctuating trend between 1992 and 1997,before attaining a steady increasing path from 1998 towards 2002. However the actual production is still low and does not contribute significantly in Zanzibar fish exports despite high potentiality. Distribution of fish catches by districts reveal that currently urban Unguja district is leading in fish production since 2001, outpacing North district which dominated before. Exports (export earnings) was the highest in 2003 because of the sea products such as sea shells and sea cucumber from the business people. The Zanzibar Poverty Reduction Plan(ZPRP Jan 2002)) 1. 2Statement of the problem. Zanzibar, having two islands namely Unguja and Pemba located in the Indian ocean have varieties of fish. The islands are accessible by sea, having two ports in Unguja and Pemba making it easier to export fish products, these factor facilities are important for developing fishing industry. According to Tanzania Reproductive and Child Health Survey(1999) about 35. 8% of under five children are stunted of which 12. 2% are severely stunted. For Pemba 46. 25% of under five children are stunted, while for ungula it is 27. 5% . The situation calls for a study to establish how the fishing industry can be improved (e. g. by identifying appropriate technology and reliable markets) to get rid of malnutrition, reduce poverty, increase export proceeds, increase tax revenue and increase employment opportunities. 1. 3. Significant of the study The finding of this research will encourage the concerned authorities to perform their duties that is by improving the fishing industry in order to reduce poverty and exercise their professions and responsibilities towards controlling the current problem which is poverty.
Further more the study will collect information from different sources and use the findings to alert the authorities concerned about the fishing industry and how it will contribute towards reduction poverty. 1. 4 Scope of the study The study will take about 2 weeks in February and will cover Zanzibar as a case study which will be the inclusion of Unguja as it analyses the contribution of fishing industry towards poverty reduction in Zanzibar. 1. 5Objective of the research 1. 5. 1General objectives. To estimate the extent of fishing industry on poverty reduction in the study area 1. 5. Specific objectives The study will seek to achieve the following: To evaluate the potentiality of fishing in Zanzibar economy To identify problems and opportunities in fishing industry and its marketing in the study area To assess the applicability of fishing industry towards the reduction of poverty in the study area 1. 6 . Hypothesis of the study The following will be tested in order to assess the validity of both overall and specific objectives. Does the fishing industry leads to the poverty reduction? That is: Null hypothesis (HO): Fisheries improvement is the determinant for poverty reduction.
Alternative Hypothesis (Hi): fisheries improvement is not a determinant of poverty reduction. CHAPTER TWO LITERATURE REVIEW. 2. 0. INTRODUCTION This study comprises literature review about the contribution of fishing industry towards the poverty reduction in Zanzibar. These reviews include books, journals, articles and details from the Ministry of Agriculture and Fisheries Department. This chapter is divided into two parts. The first part deals with Fishing Industry and the second part is a review in Poverty Reduction. 2. 1 Definition of fishing
From the encyclopedia (Britanica) ; – Fishing involves the recovery of foods and other valuable resources from bodies of water. Fishing involves the extraction of all marine products. – Fishery; is harvesting of as a commercial enterprise or the location or season of commercial fishing. 2. 1 Background of fishing Industry (FDZ) Government of Zanzibar’s involvement in fishing activities started many years ago but because of abundant resources, few fishers and primitive gear, fisheries activities were not considered important.
Before 1964 revolution, there was a private fishing corporation under management of the Greeks, which was charged with supervision of all fishing activities in Zanzibar. After the 1964 Revolution, the Government of Zanzibar nationalized the corporation as established it as public enterprise charged with the responsibility of monitoring fishing activities and improving working conditions of the fisher folk. In 1974,the Revolutionary Government of Zanzibar formed the Department of Fisheries, under the Ministry of Agriculture, Livestock and Environment.
Besides other functions, and key responsibility of the department was directed to supervise and modernize fishery activities. In order to modernize fishing, the department of Fisheries established several centres for coordinating, simplifying and promoting fishing activities. 2. 2 Fishing in Zanzibar’s economy Unguja and Pemba are surrounded by rich marine resources, the people of Zanzibar utilize marine products for subsistence and as a source of income, with fish being among the most important resources and socio-economic activities of the people in Zanzibar economy.
Fishing has been conducted in the islands since the dawn of humanity and still continues to be an important coastal activity. Fishing provides employment for men and women and almost all age groups. Fishing activity employs an average of 25% of the population as artisanal fishers and account for an average of 4. 5% of GDP. According to the recent data provided by Ministry of Agriculture, Livestock and Environment( Department of Fisheries and Marine Resources – Zanzibar),there has been gradual decrease in the GDP accounted from fishing sector.
In 2004 GDP for fish had increased to 5% and it kept on increasing in 2005 reaching 5. 9% but from there it went on decreasing in the year 2006 reaching 4. 9%. So in my study i will try to look for the reason of decreasing in this fishing industry and try to look at which ways the government suppose to do to improve it and help the people of Zanzibar that is reduce the poverty. 2. 3 POVERTY REDUCTION Before getting to the concept of Poverty Reduction, the meaning of Poverty should be understood. 2. 4 Definition of poverty According to the World’s encyclopaedia 9:652:3a
Poverty is the condition that is said to exist when people lack the means to satisfy their basic needs which are necessary for survival. According to Gerald M. Meir and James E. Rauch in the book Leading Issues in Economic Development (seventh edition) ; Poverty is concerned with the absolute standard of living of a part of the society. According to Michael Todaro and Stephen Smith in their book Economic Development ; Poverty is the number of people who are unable to command sufficient resources to satisfy basic needs. it’s a total number living below a specified minimum level of real income – an international poverty line.
Most current projections call for the number of persons living in poverty to rise over the current decade but this outcome depends on two factors; – the rate of economic growth – the level of resources devoted to poverty programs and the quality of those programs. 2. 4. 0: Growth and poverty Rapid growth is bad for the poor because they would be bypassed by the structural changes of modern growth. I will try to look how the public expenditures required for the reduction of poverty would entail the reduction in the rate of growth.
The poor tend to spend additional income on improved nutrition, education for children, improvements in housing conditions and other expenditures that especially at poverty levels represent investments rather than consumption. Reasons why policies focused towards reducing poverty levels need not to lead in slower rate of growth ; i. widespread poverty creates conditions in which the poor have no access to credit, are unable to finance their children’s education and the absence of physical or monetary investment opportunities. ii.
The low incomes and the low level of living for the poor which are manifested in poor health, nutrition and education can lower their economic productivity and lead to the slower growing economy. iii. Raising the income levels of the poor will stimulate an overall increase in the demand for locally produced necessity products like food and clothing whereas the rich tend to spend on luxury goods. iv. A reduction of mass poverty can stimulate healthy economic expansion by acting as a powerful material and psychological incentive to widespread public participation in the development process. CHAPTER THREE . 0: METHODOLOGY. The methodology that will be applied in my study has been chosen in order to acquire information and deduce conclusions about the contribution of fishing industry towards poverty reduction and the alternative measures which should be taken in order to make sure that they adapt to this problem. 3. 1 AREA OF THE STUDY The study will be conducted at mkokotoni fishing site in Zanzibar and the Department of fisheries, where fishermen and officers of fisheries were involved. 3. 2 TARGETED POPULLATION The targeted populations are officials from the Department of Fisheries and the fishermen.
As it is not easy to deal with each individual in the department and all the fishermen available in Zanzibar, a research used sampling method that is simple random to get actual respondents and in reducing sampling errors. A sample of 10 to 20 fishermen will be drawn from the population. 3. 3 PURPOSE OF THE STUDY AND TYPE OF INVESTIGATION The main purpose of this study Is to obtain an insight into the current contribution of fishing industry towards poverty reduction in Zanzibar. For the above reason, this research will take an exploratory approach.
According Sekaran (2002:123) an exploratory study is undertaken when not much is known about the situation at hand, or when no information is available on how similar problems or research issues have been solved in the past. The aim will be to gain familiarity with the issues, and to gain a deeper understanding about the topic and to come out with the suggestive measures which should be taken to adapt to this problem of fishing industry. 3. 4 DATA COLLECTION. For the purpose of this research, and in order to achieve the objectives data will be collected and will use both primary and secondary data.
The secondary data will contribute toward the formation of background information, needed by both the researcher in order to build constructively the project and the reader to comprehend more thoroughly the survey outcome. Primary data will be collected in two ways. Firstly, a questionnaire survey will be conducted with researcher visiting the area. Secondly, interviews will be also carried out with I will go to the fishermen and asking them about how there work has contributed towards reduction of poverty. 3. 5 SAMPLING DESIGN Ideally I wanted to study the entire population of fishermen.
However, it will be impossible and unfeasible to do this and therefore I must settle for a sample. According to Kothari C. R, sample is a portion of elements taken from a population, which is considered to be representative of the population. In order to collect primary data the questionnaires survey technique will be used. For the purpose of this study I will use both simple random probability sampling and purposive random sampling. Under simple random sampling each of the fisherman found in the area visited will be able to provide with information on how he/she contribute to reduction of poverty.
Also under purposive random sampling I will be responsible of setting some criteria on whom to interview. 3. 6 QUESTIONNAIRE SURVEY In order to achieve my goal of this study and get relevant information about this problem I will use both closed and open ended questions. Under the closed ended questions I will narrow the field inquiry and will choose among the fixed responses. This will enable me to analyze my data easier since the responses will be easier to compare. Also the open ended questions will enable me to get new ideas and varieties of information about the problem. 3. 7 THE INTERVIEW SURVEY
The technique of personal interviewing is undertaken in order to reach the objectives since it is the most versatile and productive method of communication, enabled spontaneity, and also provided with: “The skill of guiding the discussion back to the topic outlined when discussions are unfruitful though it has the disadvantages of being very costly time consuming and can introduce bias through desires of the respondent to please the interviewer. 3. 8 DATA ANALYSIS After collecting the data from the field I will use Microsoft excel and Statistical Packages for Social Sciences (SPSS).
These methods will enable me to draw a valid conclusion of what I will find in the field in relation to the objectives I have put forward. 3. 9 CONTRIBUTION OF THE STUDY As it is the purposes of this study that it helps to investigate the contribution of fishing industry towards poverty reduction. When I complete this research I will add an important value on the academic part. Also the purpose of this study is to enable me understand on how I can conduct research on different cases. MODEL OF THE STUDY In my study as the qualitative research there is the need of using a model to est the result of the research, here the multiple regression model will be used for the test of my research. The model of my study will be as follows: Y =? 0 + ? 1X1 + ? 2X2 + ? 3X3 + ? 4X4 + µ Where; Y – stands for Income X1 – stands for education level X2 – stands for technological level X3 – stands for age of the fisherman X4 – stands for financial assistance X5 – stands for family size µ – stands for Error term as Y stands for dependent variable that is it depends on the changes of its explanatory variables. Independent variables can be explained as follows;
Education level- that is if the education level of fisheries is high we expect to have more income and if its low expect low income. Technological level – that is the use of more advanced technology leads to increase in income. Age- as how ages leads to increase in income, that as ages goes up or down leads to increase in income. Financial assistance- that is how the government financially assists this sector as assisted more we expect for more income. Family size – Family size of a respondent was one variable (continuous variable) proposed to influence participation decision.
The more number of family members an individual had the more probable to participate in fishing. This is because he will have a labor source. BIBLIOGRAPHY Gerald M. Meier,et al, “ Leading issues in Economic Development” “seventh edition” Humphrey P. B. et al,. Zanzibar: The challenges of globalization and Poverty reduction Jiddawi N, M. (1997) : Fisheries stock Assessment in the Traditional Fisheries sector. Kothari C. (2004) “Research Methodology: methods and techniques” New Age international (P) limited, New Delhi. Michael P. T,et al, “Economic Development” Mkenda, A. 2001 “Fishery Resources and welfare in Rural Zanzibar”
World’s encyclopaedia (Britanica) QUESTIONNARES 1. What is your name?……………………………………………………………… Sex; male ( ) female( ) AGE: 18 – 25| | 26 – 37| | 37 – 57| | Above 57| | MARITAL STATUS: Single| | Married| | Divorced| | Widowed| | Others| | 2. What is your level of education? | Level of education| Tick (v)| A| Primary level | | B| Secondary level| | C| Advanced level| | D| University level| | E| None| | 3. How many children do u have?……………………………………. Are they participate with you in fishing. Yes ( ) No ( ) 4.
For how long have you been working in fishing?…………………………………… …………………………………………………………………………………………………………………. 5. How do you see the development of fishing? Put ( v ) where applicable Increasing/developing? ( ) wasting? ( ) Or you’re not sure? ( ) Specify your answer……………………………………………………………………………………………. ………………………………………………………………………………………………………………………….. 6. Are you fishing only here or you are shifting? If shifting, why?…………………………………………………………………………………….. ………………………………………………………………………………………………………………………….. . Which tools are you using for fishing? i). Advanced tools ( ) ii). Traditional tools ( ) if others specify………………………………………………………………………………………………… 8. Are you the owner of the tools you are using? Yes ( ) / No ( ) 9. Is there any other activities you are doing in spite of fishing? Yes( ) / No ( ) If yes tick (v) where applicable i. Farming| | ii. Hunting| | iii. Livestock keeping| | iv. Others| | If others, specify……………………………………………………………………………………………………. 10. Do you have the market for your fishes? Yes( ) / No ( ) Tick (v) where applicable Internationally| |
Nationally | | 11. How much money do you get for single fishing? ……………………. 12. Do you thing this work of fishing is reducing the hardship of life? Yes ( ) / No ( ) How, ……………………………………………………………………………………………….. …………………………………………………………………………………………………………. 13. Why do you think fishing has been decreasing in these recently years? …………………………………………………………………………………………………………………… ……………………………………………………………………………………………………………………. ……………………………………………………………………………………………………………………. THE UNIVERSITY OF DODOMA PROPOSAL The contribution of fishing industry towards poverty reduction in Zanzibar. BY Mussa, Hanifu

Contribution of Fishing Industry Towards Poverty Reduction in Zanzibar

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Mth Sl Type Ii Portfolio – Fishing Rods

Mth Sl Type Ii Portfolio – Fishing Rods.

Math Summative: Fishing Rods Fishing Rods A fishing rod requires guides for the line so that it does not tangle and so that the line casts easily and efficiently. In this task, you will develop a mathematical model for the placement of line guides on a fishing rod. The Diagram shows a fishing rod with eight guides, plus a guide at the tip of the rod. Leo has a fishing rod with overall length 230 cm. The table shown below gives the distances for each of the line guides from the tip of his fishing rod.
Define suitable variables and discuss parameters/constraints. Using Technology, pot the data points on a graph. Using matrix methods or otherwise, find a quadratic function and a cubic function which model this situation. Explain the process you used. On a new set of axes, draw these model functions and the original data points. Comment on any differences. Find a polynomial function which passes through every data point. Explain you choice of function, and discuss its reasonableness. On a new set of axes, draw this model function and the original data points. Comment on any differences.
Using technology, find one other function that fits the data. On a new set of axes, draw this model function and the original data points. Comment on any differences. Which of you functions found above best models this situation? Explain your choice. Use you quadratic model to decide where you could place a ninth guide. Discuss the implications of adding a ninth guide to the rod. Mark has a fishing rod with overall length 300cm. The table shown below gives the distances for each of the line guides from the tip of Mark’s fishing rod.

Guide Number (from tip)
How well does your quadratic model fit this new data? What changes, if any, would need to be made for that model to fit this data? Discuss any limitations to your model. Introduction: Fishing rods use guides to control the line as it is being casted, to ensure an efficient cast, and to restrict the line from tangling. An efficient fishing rod will use multiple, strategically placed guides to maximize its functionality. The placement of these will depend on the number of guides as well as the length of the rod. Companies design mathematical equations to determine the optimal placement of the guides on a rod.
Poor guide placement would likely cause for poor fishing quality, dissatisfied customers and thus a less successful company. Therefore it is essential to ensure the guides are properly placed to maximize fishing efficiency. In this investigation, I will be determining a mathematical model to represent the guide placement of a given fishing rod that has a length of 230cm and given distances for each of the 8 guides from the tip (see data below). Multiple equations will be determined using the given data to provide varying degrees of accuracy. These models can then potentially be used to determine the placement of a 9th guide.
Four models will be used: quadratic function, cubic function, septic function and a quadratic regression function. To begin, suitable variables must be defined and the parameters and constraints must be discussed. Variables: Independent Variable: Let x represent the number of guides beginning from the tip Number of guides is a discrete value. Since the length of the rod is finite (230cm) then the number of guides is known to be finite. Domain = , where n is the finite value that represents the maximum number of guides that would fit on the rod.
Dependent Variable:
Let y represent the distance of each guide from the tip of the rod in centimetres. The distance of each guide is a discrete value. Range = Parameters/Constraints: There are several parameters/constraints that need to be verified before proceeding in the investigation. Naturally, since we are talking about a real life situation, there cannot be a negative number of guides (x) or a negative distance from the tip of the rod (y). All values are positive, and therefore all graphs will only be represented in the first quadrant. The other major constraint that must be identified is the maximum length of the rod, 230cm.
This restricts the y-value as well as the x-value. The variable n represents the finite number of guides that could possibly be placed on the rod. While it is physically possible to place many guides on the rod, a realistic, maximum number of guides that would still be efficient, is approximately 15 guides. Guide Number (from tip) Distance from Tip (cm) 0* 1 2 3 4 5 6 7 8 n** 0 10 23 38 55 74 96 120 149 230 *the guide at the tip of the rod is not counted **n is the finite value that represents the maximum number of guides that would fit on the rod.
Neither of the highlighted values are analyzed in this investigation, they are only here for the purpose of defining the limits of the variables. The first step in this investigation is to graph the points in the table above (excluding highlighted points) to see the shape of the trend that is created as more guides are added to the rod. From this scatter plot of the points, we can see that there is an exponential increase in the distance from the tip of the rod as each subsequent guide is added to the rod. Quadratic Function: The first function that I shall be modeling using the points of data provided is a quadratic function.
The general equation of a quadratic formula is y = ax2 + bx + c. To do this, I will be using three points of data to create three equations that I will solve using matrices and determine the coefficients: a, b and c. The first step in this process is to choose three data points that will be used to represent a broad range of the data. This will be difficult though since there are only three out of the eight points that can be used. Therefore, to improve the accuracy of my quadratic function, I will be solving two systems of equations that use different points and finding their mean. Data Sets Selected: Data Set 1 = {(1,10), (3,38), (8,149)}
Data Set 2 = {(1,10), (6,96), (8,149)} These points were selected for two main reasons. First, by using the x-values 1 and 8 in both sets of data, we will have a broad range of all of the data that is being represented in the final equation after the values of the coefficients are averaged. Second, I used the x values of 3 (in the first set) and 6 (in the second set) to once again allow for a broad representation of the data points in the final quadratic equation. Both of these points (3 and 6) were chosen because they were equal distances apart, 3 being the third data point, and 6 being the third from last data point.
This ensured that the final averaged values for the coefficients would give the best representation of the middle data points without skewing the data. There will be two methods that will be used to solve the system of equations, seen below. Each method will be used for one of the systems being evaluated. Data Set 1 = {(1,10), (3,38), (8,149)} In the first data set, the data points will form separate equations that will be solved using a matrices equation. The first matrix equation will be in the form: Where A = a 3×3 matrix representing the three data points X = a 3×1 matrix for the variables being solved B = a 3×1 matrix for the y-value of the three equations being solved. This matrix equation will be rearranged by multiplying both sides of the equation by the inverse of A: Since A-1*A is equal to the identity matrix (I), which when multiplied by another matrix gives that same matrix (the matrix equivalent of 1), the final matrix equation is: To determine the values of X, we must first find the inverse of matrix A using technology, since it is available and finding the inverse of a 3 by 3 matrix can take an inefficient amount of time.
First let us determine what equations we will be solving and what our matrices will look like. Point: (1,10) (3, 38) (8,149) A= The equation is: ,X= ,B= = Next, by using our GDC, we can determine the inverse of matrix A, and multiply both sides by it. Therefore we have determined that the quadratic equations given the points {(1,10), (3,38), (8,149)} is . Data Set 2 = {(1,10), (6,96), (8,149)} Point: (1,10) (6, 96) (8,149) A= ,X= ,B= The second method that will be used to solve the second system of equations is known as Gauss-Jordan elimination.
This is a process by which an augmented matrix (two matrices that are placed into one divided by a line) goes through a series of simple mathematical operations to solve the equation. On the left side of this augmented matrix (seen below) is the 3×3 matrix A (the new matrix A that was made using data set 2, seen on the previous page), and on the right is matrix B. The goal of the operations is to reduce matrix A to the identity matrix, and by doing so, matrix B will yield the values of matrix X. This is otherwise known as reduced row echelon form. Step by step process of reduction: 1. We begin with the augmented matrix. . Add (-36 * row 1) to row 2 3. Add (-64 * row 1) to row 3 4. Divide row 2 by -30 5. Add (56 * row 2) to row 3 6. Divide row 3 by 7. Add ( * row 3) to row 2 8. Add (-1 * row 3) to row 1 9. Add (-1 * row 2) to row 1 After all of the row operations, matrix A has become the identity matrix and matrix B has become the values of matrix X (a, b, c). Therefore we have determined that the quadratic equations given the points {(1,10), (6,96), (8,149)} is . Averaging of the Two Equations The next step in finding our quadratic function is to average out our established a, b, and c values from the two sets data.
Therefore we have finally determined our quadratic function to be: Rounded to 4 sig figs, too maintain precision, while keeping the numbers manageable. Data points using quadratic function Guide Number (from tip) Quadratic values Distance from Tip (cm) Original – Distance from Tip (cm) 1 10 2 22 3 37 4 54 5 74 6 97 7 122 8 149 10 23 38 55 74 96 120 149 New values for the distance from tip were rounded to zero decimal places, to maintain significant figure – the original values used to find the quadratic formula had zero decimal places, so the new ones shouldn’t either.
After finding the y-values given x-values from 1-8 for the quadratic function I was able to compare the new values to the original values (highlighted in green in the table above). We can see that the two values that are the exact same in both data sets is (1,10) and (8,149) which is not surprising since those were the two values that were used in both data sets when finding the quadratic function. Another new value that was the same as the original was (5,74). All other new data sets have an error of approximately ±2cm.
This data shows us that the quadratic function can be used to represent the original data with an approximate error of ±2cm. This function is still not perfect, and a better function could be found to represent the data with a lower error and more matching data points. Cubic Function: The next step in this investigation is to model a cubic function that represents the original data points. The general equation of a cubic function is y = ax3 + bx2 + cx + d. Knowing this, we can take four data points and perform a system of equations to determine the values of the coefficients a, b, c, and d.
The first step is to choose the data points that will be used to model the cubic function. Similarly to modeling the quadratic function, we can only use a limited number of points to represent the data in the function, only in this case it is four out of the eight data points, which means that this function should be more precise than the last. Once again I plan on solving for two sets of data points and finding their mean values to represent the cubic function. This is done to allow for a more broad representation of the data within the cubic function. Data Sets Selected: Data Set 1: {(1,10), (4,55), (5,74), (8,149)}
Data Set 2: {(1,10), (3,38), (6,96), (8,149)} Both data sets use the points (1,10) and (8,149), the first and last point, so that both data sets produce cubic functions that represent a broad range of the data (from minimum to maximum). The other points selected, were selected as mid range points that would allow for the function to represent this range of the data more accurately. When modeling a cubic function or higher, it is difficult to do so without using technology to do the bulk of the calculation due to large amounts of tedious calculations that would almost guarantee a math error somewhere.
Therefore, the most accurate and fastest way to perform these calculations will be to use a GDC. In both data sets, the reduced row echelon form function on the GDC will be utilized to determine the values of the coefficients of the cubic functions. The process of determining the values of the coefficients of the cubic function using reduced row echelon form is similar to process used for the quadratic function. An x-value matrix A (this time a 4×4 matrix), a variable matrix X (4×1) and a y-value matrix B (4×1) must be determined first. The next step is to augment matrix A and matrix B, with A on the left and B on the right.
This time, instead of doing the row operation ourselves, the GDC will do them, and yield an answer where matrix A will be the identity matrix and matrix B will be the values of the coefficients (or matrix X). Data Set 1: {(1,10), (4,55), (5,74), (8,149)} (1,10) (4, 55) (5, 74) (8,149) A1 = , X1 = , B1 = We begin with the augmented matrix or matrix A1 and matrix B1. Then this matrix is inputted into a GDC and the function “rref” is selected. After pressing enter, the matrix is reduced into reduced row echelon form. Which yields the values of the coefficients. Data Set 2: {(1,10), (3,38), (6,96), (8,149)} (1,10) (3, 38) 6, 96) (8,149) A2 = , X2 = , B2 = We begin with the augmented matrix of matrix A2 and matrix B2. Then the matrix is inputted into a GDC and the function “rref” After pressing enter, the matrix is reduced into reduced row echelon form. Which yields the values of the coefficients. The next step is to find the mean of each of the values of the coefficients a, b, c, and d. Therefore we have finally determined our cubic function to be: Once again rounded to 4 significant figures. Updated Data table, including cubic function values. Guide Number (from tip) Quadratic values Distance from Tip (cm) 1 10 2 22 3 37 4 54 5 74 6 97 122 8 149 Cubic values Distance from Tip (cm) Original – Distance from Tip (cm) 10 23 38 54 74 96 121 149 10 23 38 55 74 96 120 149 New values for the distance from tip were rounded to zero decimal places, to maintain significant figure – the original values used to find the quadratic formula had zero decimal places, so the new ones shouldn’t either. The y-values of the cubic function can be compared to that original data set values to conclude whether or not it is an accurate function to use to represent the original data points. It appears as though the cubic function has 6 out of 8 data points that are the same.
Those points being, (1,10), (2,23), (3,38), (5,74), (6,96), (8,149). The three data points from the cubic function that did not match only had an error of ±1, indicating that the cubic function would be a good representation of the original data points, but still has some error. We can further analyze these points by comparing the cubic and quadratic function to the original points by graphing them. See next page. By analyzing this graph, we can see that both the quadratic function and the cubic function match the original data points quite well, although they have slight differences.
By comparing values on the data table, we find that the quadratic function only matches 3 of the 8 original data points with an error of ±2, while the cubic function matches 6 of the 8 points with an error of just ±1, which is as small an error possible for precision of the calculation done. Both functions act as adequate representations of the original points, but the major difference is how they begin to differ as the graphs continue. The cubic function is increasing at a faster rate than the quadratic function, and this difference would become quite noticeable over time.
This would mean that if these functions were to be used to determine the distance a 9th guide should be from the tip, the two functions would provide quite different answers, with the cubic functions providing the more accurate one. Polynomial Function: Since it is known that neither the quadratic, nor the cubic function fully satisfy the original data points, then we must model a higher degree polynomial function that will satisfy all of these points. The best way to find a polynomial function that will pass through all of the original points is to use all of the original points when finding it (oppose to just three or four).
If all eight of the points are used and a system of equations is performed using matrices, then a function that satisfies all points will be found. This is a septic function. To find this function, the same procedure followed for the last two functions should be followed, this time using all eight points to create an 8×8 matrix. By then following the same steps to augment the matrix with an 8×1 matrix, we can change the matrix into reduced row echelon form to and find our answer. In this method, since we are using all eight points, the entire data set is being represented in the function and no averaging of the results will be necessary.
The general formula for a septic function is . Data Set: {(1,10), (2,23), (3,38), (4,55), (5,74), (6,96), (7,120), (8,149)} (1,10) (2,23) (3,38) (4,55) (5,74) (6,96) (7,120) (8,149) A=,X= ,B= , Augment matrix A and matrix B and perform the ‘rref’ function The answers and values for the coefficients = The final septic function equation is This function that include all the original data points can be seen graphed here below along with the original points. Updated Data table, including septic function values Guide Number (from tip) Quadratic values Distance from Tip (cm) Cubic values Distance from Tip (cm)
Septic values – Distance from Tip (cm) Original – Distance from Tip (cm) 1 10 2 22 3 37 4 54 5 74 6 97 7 122 8 149 10 23 38 54 74 96 121 149 10 23 38 55 74 96 120 149 10 23 38 55 74 96 120 149 New values for the distance from tip were rounded to zero decimal places, to maintain significant figure – the original values used to find the quadratic formula had zero decimal places, so the new ones shouldn’t either. By looking at the graph, as well as the data table (both seen above), we can see that, as expected, all 8 of the septic function data points are identical to that of the original data.
There is less than 1cm of error, which is accounted for due to imprecise (zero decimal places) original measurements. Therefore we now know that the septic function that utilised all of the original data points is the best representation of said data. Other Function: The next goal in this investigation is to find another function that could be used to represent this data. The other method that I will use to find a function that fits the data is quadratic regression. Quadratic regression uses the method of least squares to find a quadratic in the form .
This method is often used in statistics when trying to determine a curve that has the minimal sum of the deviations squared from a given set of data. In simple terms, it finds a function that will disregard any unnecessary noise in collected data results by finding a value that has the smallest amount of deviation from the majority of the data. Quadratic regression is not used to perfectly fit a data set, but to find the best curve that goes through the data set with minimal deviation. This function can be found using a GDC. First you must input the data points into lists, (L1 and L2).
Then you go to the statistic math functions and choose QuadReg. It will know to use the two lists to determine he quadratic function using the method of least squares. Once the calculation has completed, the data seen below (values for the coefficients of the function) will be presented: QuadReg a = 1. 244 b = 8. 458 c = 0. 8392 With this data we can determine that the function is When graphed, this function has the shape seen below: Updated Data table, including septic function values Guide Number (from tip) Quadratic values Distance from Tip (cm) Cubic values Distance from Tip (cm)
By analyzing the graph and values of the quadratic regression function, it is evident that it is a relatively accurate form of modeling the data. Four of the eight points matched that of the original data, with an error of ±1. The most notable difference between the quadratic regression function and the quadratic function previously determined, is the placement within the data f the accurate values. The regression function matched the middle data, while the quadratic function matched the end data. It is interesting to see how two functions in the same form, found using different methods yielded opposite areas of accuracy. Best Match: The function that acts as the best model for this situation is the septic function. It is the only function that satisfies each of the original data points with its equation. Through finding the quadratic, cubic and septic functions, it was discovered that the degree of the polynomial was directly correlated to the function’s accuracy to the data.
Therefore it was no surprise that this function acts as the best fit for this data. The other cause for this septic function having the best correlation to the original data is due to the septic function being established by creating a system of equations using all of the data points. 9th Guide: Using my quadratic model, it can be determined where the optimal placement for a ninth guide would be by substituting ‘9’ in for x in the equation . Using my quadrating model, it was found that the optimal placement for a ninth guide on the rod is 179cm from the tip of the rod.
Leo’s fishing rod is 230cm long, yet his eighth guide is only 149cm from the tip of the rod. That means that there is 81cm of the line that is not being guided from the reel to first guide. By adding a ninth guide, that distance will be shortened form 81cm to 51cm. By doing this, it will be less likely for the line to bunch up and become tangled in this 81cm stretch where there is no guide. Another implication of adding another guide would be that the weight distribution of a fish being reeled in would be spread over another guide, which will allow for an easier task of reeling in the fish.
There is even enough space on the rod for a 10th guide at 211cm from the tip of the rod. This guide would once again shorten the excess line further to a point where the excess line between the reel and the first guide is shorter than line between the first and second guide. This could cause problems with reeling and casting efficiency, as that extra guide would cause slowing movement of the line. The benefit would be that once again the weight distribution of fish would be spread over a larger number of guides.
Overall, it would be beneficial to include a ninth guide to Leo’s fishing rod, but anymore will likely hinder its efficiency. Mark’s Fishing Rod: Guide Number (from tip) Distance from Tip (cm) 1 10 2 22 3 34 4 48 5 64 6 81 7 102 8 124 To see how well my quadratic model fits this new data, they must be both plotted on the same graph, seen below. My quadratic model for Leo’s fishing rod correlates with Mark’s fishing rod data for the first few values and then diverges as the number of guides increases by growing at a higher exponential rate.
The difference between Leo and Mark’s eighth guide from the tip of their respective rods is 25cm, yet both men’s first guides start the same distance from the tip of their rods. The quadratic function used to model Leo’s fishing rod does not correlate well with Mark’s fishing rod data. Changes to the model must be made for it to fit this data. The best way to find a model for Mark’s data would be to go through the same steps that we went through to determine the first quadratic formula that model’s Leo’s fishing rod.
By doing so, specific values that better represent Mark’s fishing rod data could be used to establish a better fitting function. The main limitation of my model is that is was designed as a function for Leo’s data specifically. It was created by solving systems of equations that used solely Leo’s fishing rod for data. Consequentially, the quadratic model best represented Leo’s fishing rod, which had a maximum length of 230cm, with differently spaced out guides. There were many differences between Leo and Mark’s fishing rods (such as maximum length and guide spacing) that caused my original quadratic model to not well represent Mark’s data.

Mth Sl Type Ii Portfolio – Fishing Rods

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How has the Mass Fishing Effected the Natural Resources

How has the Mass Fishing Effected the Natural Resources.
How has the Mass Fishing effected the natural resources and environment of the earth? With the depletion of fisheries and coastal environments around the world there is a corresponding reaction for a booming fishing industry that has benefitted significantly off the demand for more and more freshly resourced sea food.
But how has this effected the environment of these coastal areas that are being harvested? It has caused the utter destruction of a significant proportion of the world\’s coastal areas, and despite conservationist efforts shows no sign of stopping. The fishing industry’s exploits have had varying effects on economies and environments locally, nationally, and globally.
In many underdeveloped societies fishing is not just a way of life it is the source of life. A prime example of this is on the aboriginal reserves in Australia. In 1905 the fishing regulations around coastal areas were not a strict as they are in the present day and aboriginals were able to thrive self sustainably by fishing on their reserves in the traditional way done for centuries.

While most of this fishing was purely for the sole purpose of feeding their tribe, so normal restrictions for the harvesting of fish were not applied. It was recognized in the early 80’s that an “operation” was taking place on the aboriginal land that was taking advantage of the limited harvest limits in the reservation to harvest a massive amount of fish to sell commercially outside of the reservation.
This “operation”, was practically ignored until the early 90’s when an ecological survey discovered an enormous “drought” in aquaculture in the coastal area’s off of the reservation, this was because the “operation” had been harvesting ten times the limit applied to commercial fisheries outside of the reservation.
Because of this incident Australia passed the “1991 FAO Code of Conduct for Responsible Fisheries”, which now applied to all coastal aboriginal reservations as well as commercial fisheries to prevent over harvesting mainly in western Australia. At first this received an enormous backlash, mainly from the aboriginals who had abused their rights to run the “operation” but is still in effect to the present day.
This is a primary example of how even local fisheries can have a drastic impact on the environment and must be strictly regulated in order to preserve the environment. While this was a severely negative event for this local coastline the aboriginal tribe that, was responsible for this “operation”, gained a significant economical product because of their efforts.
The first signs of an unhealthy relationship between the fishing industry and the environment in America was documented to be in the 20th century when fleets of cod fishers in New England nearly depleted Cape Cod’s entire stock of Cod in less than decade. This was the first example of mass fishing that resulted in an ecosystems primary organism almost being completely wiped out.
To coincide with this recreational fishing in this area continued even after the main event of mass fishing ceased further decimating the cod populations. This occurred during a time when fishing regulations were virtually nonexistent in that there were none, and fishing was open to anyone with the capabilities and man power to harvest the ocean. The events at Cape Cod led to several things happening across a newly developing America.
The first being that all the cod harvested had to be processed, canned, which produced many jobs benefiting the economy in the area for around ten years and starting an almost cod exclusive seafood market in the New England region. Albeit at the cost of the cod population in the area for generations, almost 90%.
If this situation had been managed better and restrictions had been put on the amount of fish able to be caught per vessel such a dramatic decrease would likely not have occurred and the environment would have had a more stable decrease of Cod. This had a national effect on the coasts of America because it was the start of mass fishing in this part of the world.
In the Pacific Ocean mass fishing is one of the most prominent industries in the world. Mass fishing ranges from the American west coast, to the Japanese coastline and extends to everywhere in between. The Pacific Ocean makes over 30% of the world and despite the common idea that it is an endless supply of seafood to support the fishing industry, that is not the case and it is drastically over fished. The fisheries and fishing vessels in this ocean providing much of the world\’s seafood.
This is not without its own cost; despite this oceans vastness its aquatic life density has decreased around 20% in the past thirty years. Several species have been harvested to the brink of extinction namely the blue finned tuna, and several whale species. The methods of fishing involved are extremely efficient in that it is estimated in 2017 over seventy billion fish where harvested from the Pacific Ocean alone.
This has had a drastic effect on ecosystems globally, with the decrease and increase of certain fish skyrocketing and plummeting in such a short geological amount of time all aquatic ecosystems in this portion of the world are in disarray. With the number of vessels harvesting this ocean there is no easy way to regulate the amount of fish harvested, weight limits are already in place to try to counteract the amount of ecological loss. The industry overall that relies on this ocean is too large to regulate because of its size, global influence, and its support of economies around the globe.
Mass fishing is an ever-increasing problem around the world and shows no signs of stopping. While it does support entire countries\’ economies it is at a great cost to the world\’s environment. There is not an easy answer to how to solve this problem because of the vastness of the issue, but the effects are ever increasing and eventually it will be present to all the dangers of over harvesting our oceans.

How has the Mass Fishing Effected the Natural Resources

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A Childhood Experience: My First Fishing Trip

A Childhood Experience: My First Fishing Trip.
Carefully applying sunscreen over every bare inch of my sixty-two-pound body, I prepped myself for the strenuous mission that was before me. I was ready to complete the first impediment of fishing, catching my very first fish. I was eight and confident that my lean, frail body could overcome any obstruction.
Alluring me intensely was my yearning to catch a fish. In fact, I was so fully captivated in my own thinking that the trip to my grandfather’s special fishing section seemed momentary. The swift break in the purring of the engine put me back into the real world. I instantly gathered my fishing pole.
Hanging the end of my fishing rod over the rim of the boat, I let go of the beam on the reel and dropped the plastic lure in the water. When I let enough of the line out and laid the fishing rod in a holder, I laid back and waited for a strike on the lure. The low purr of the motor at trawling speed only increased my angst, like the background music to a horror movie.

Then it happened. A strong jerk on the line yanked me up to my feet quicker than a blink of the eye. My weak strength was so dominant that when I pulled on the rod, I almost went down head-first over the boat, into the water. Although anxiety flowed heavy through my veins, after ten minutes my inequitable strength and my uncanny will were decreasing frequently. As soon as I was totally ready to submit to the fish and just give up the fish did an amazing stunt.
I observed the mahi-mahi leaping across the water’s surface. The mahi-mahi glistened with glittering colors of green, yellow, and blue. The glamorous fish swam back to the ocean in a burst of foam. With this marvelous spectacle, the fish was converted from a sad victim to an amazing specimen of life. I wanted so badly to touch the beautiful fish and share the amazing bond that a fisherman feels for their first kill. I wanted to capture that fish by any means.
The battle only persisted for three minutes and it was three minutes of which I will never lose memory of. When the fish came close to the boat, I was more energetic than I’d felt when the fish first attacked. At my grandfather’s signal, I caught the fish and reeled it into a basket at the bottom of the boat.
I was almost exploding with elation. We took the fish out of the net and it plopped on the floor of the boat with an empty thump, and my mouth dropped down with it. Within minutes, the fish’s sparkle, color and life completely faded. Instead, there was blood. Lots of it. It squirted from its mouth and gills.
Eventually, the boat was covered with the red bodily fluid of the mahi-mahi. As it lay twitching helplessly, I felt nauseated, disgusted, and sad. Even with my grandfather’s applause and approval, I rode to shore in absolute silence. I just kept thinking about how I was the sole cause of the fish’s death. I wasn’t expecting to feel such a great hurt for something so small and insignificant.
On the way home was a bit of awkward silence. So, I decided to break it by telling my grandfather how much of a great time I had with him and enjoyed spending this time with me. I told him how much I appreciated the experience, but I don’t ever want to go fishing again. I thought I would like catching fish unfortunately it just didn’t work out for me. Unsurprisingly my grandfather took no offense to what I told him. He agreed and suggested that we can try different things together that I actually like. I kind of expected my grandfather to react this way because he’s so kind-hearted and understanding. He remembered what it was like for him when he had his very first kill.
After all, it was my first kill. Yes, I know that sounds funny because it is just an animal. In retrospect, I am content with reacting to the situation as I believe any little girl would react. That was my first and last experience of going fishing. Although my opinions about many things, including hunting and fishing have altered a lot since that day, I’m glad I got to experience it. Even though I’ll never kill a fish again, I can’t say I won’t ever eat one ha-ha.

A Childhood Experience: My First Fishing Trip

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