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\title{Regulated Agricultural Marketing Systemfor Economic Development of Jajpur District in Orissa(India)-An Empirical Analysis}
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             \author[1]{Dr. Priti Ranjan  Hathy}

             \affil[1]{  North Orissa University, Baripada, Orissa}

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\date{\small \em Received: 15 December 2012 Accepted: 3 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


Agriculture has played a key role in the process of socio-economic development of our country. Agriculture has all along been the most crucial sector of the Indian economy and even today agriculture and allied activities make the single largest attribution to Gross Domestic product accounting for nearly one fourth of the total. In Odisha, both Agriculture and Animal Husbandry provides employment directly or indirectly to around 65 per cent of the total workforce. It is ironic that Indian agriculture sector is moving in a reverse direction different from that of the non-agriculture sections. This tendency has been earmarked in the country throwing the responsibility to the agriculture market set ups failure to provide incentives to the former for more production and to stay on profession.Keywords: regulated market committee(RMC), e-agriculture, agricultural knowledge based marketing systems (akms), orissa state agricultural marketing board(osam),analysis & analysis of variance (anova.

\end{abstract}


\keywords{regulated market committee(RMC), e-agriculture, agricultural knowledge based marketing systems (akms), orissa state agricultural marketing board(osam)}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
griculture is the backbone of any developing economy like India. Because majority of the population depends directly or indirectly on it. Since many decades, it occupies a place of pride in India. Agriculture has played a key role in the process of socio-economic development of our country. The sector accounts for about 33 per cent of the Gross Domestic product and almost two thirds of the population depends on this sector for survival. It accounts for 8.56 per cent of India exports. About 43 per cent of India's geographical area is used for agricultural activity. This sector works in concert with other sectors to produce faster growth, reduce poverty and sustain the Lecturer in Commerce, Brahmabarada Mahavidyalaya, Controller of Examination, Fakir Mohan university, Balasore, Orissa.\par
Dept. of Information Technology, Government Polytechnic, Bhubaneswar, Orissa, India. e-mail: pritiranjanhathy@yahoo.com environment. Agriculture has all along been the most crucial sector of the Indian economy and even today agriculture and allied activities make the single largest attribution to Gross Domestic product accounting for nearly one fourth of the total. In Odisha, both Agriculture and Animal Husbandry contributed 22.46 per cent of the Net Domestic product of the State in 2012-2013 and provided employment directly or indirectly to around 65 per cent of the total workforce.\par
Kuzmets classifies the contributions made by agriculture to economic growth as (1) product contribution (2) Market contribution (3) Factor contribution and (4) Foreign Exchange Contribution. Odisha is primarily an agrarian economy. In the state, Agriculture has been proved as the dominant sector and about 73 per cent of total main workers are engaged in agriculture. Being a state of villages, about 87 per cent of total population lives in rural areas  {\ref [1]}. This is a study of broad agricultural marketing in the economic development in Orissa with a special reference to Jajpur district during the period 2004 to 2012. The significant of agricultural sector in Orissa economy has grown considerably. For reaching changes have taken place in agricultural economy of the country. Not only there has been growth and diversification in the agricultural marketing complex but the structure of agricultural marketing structure has become more balanced. As Owens and shaw very apply pointed out Agricultural Development is first of all a human problem, not a technical problem. If all the farmers have access to production inputs, the financial system, the market and agricultural knowledge then they can improve the state of agriculture. But most of farmers lack access to a market system and thus lack both the resources and the incentives to modernize their production methods." So it has been realised that marketing of agricultural produce is essential to provide an incentive to the farmers and to improve their standard of living. It is a matter of greater interest not only to the farmers but to the consumers and the middlemen also. The National Commission on Agriculture, in 1976, admits the needs for agricultural marketing in the country \hyperref[b1]{[2]}\hyperref[b2]{[3]}.\par
non-agriculture sections. This tendency has been earmarked in the country throwing the responsibility to the agriculture market set ups failure to provide incentives to the former for more production and to stay on profession. In our state, Odisha comes under such tragic scenario and so also the economic development of the state logs behind the other states of India. This not only impressed to conduct a survey but also to find out the causes and suitable measures to eradicate the problem \hyperref[b3]{[4]}\hyperref[b4]{[5]}. a) Objectives of this thesis Gravity of the problem and its pervasive significance in the economy of the state, Odisha in general and Jajpur district in particular to have induced in the plan of the present study.The research study is proposed to be undertaken keeping in view the following objectives. 1. To evaluate the role of agricultural marketing for the development of rural economy of the district and the steps taken by state government in agricultural marketing. 2. To assess the regulated agricultural knowledge based marketing system. 3. To give some suggestions for the improvement of efficiency and transparency in the marketing of agricultural produce.\par
The proposed study as purely analytical and empirical in character is based on both primary and secondary data. Besides descriptive survey method can also be adopted for the study. As the targeted area is confined to Jajpur district and the district consists of 10 blocks such as Barchana, Bari, Binijharpur, Dangadi, Dasarathpur, Dharmasala, Jajpur, Korei, Rasulpur and Sukinda which are covered under the study to explain the standard of living situation of the farmers residing in these areas. As all the blocks are not convenient to go for the detailed survey, so as to convenient point of view, purposive sampling method can be justified to fulfill the aims and objectives of the research study \hyperref[b5]{[6]}. 
\section[{IV.}]{IV.} 
\section[{Nature and Source of Data}]{Nature and Source of Data}\par
The study is based on the data collected from various primary and secondary sources. In the course of analyzing the issues, the primary data are collected through two sets of questionnaires and one set of interview schedule were developed to obtain data from the producer farmers, consumers and officials associated with the agricultural marketing activities. Potential surveys and techno-economic survey have also been consulted. The study has also been made from the published magazines like District statistical Abstract (A Govt. of Odisha Publication) and Economic Review etc.Apart from this, relevant informationhas been sources of certain government and non-government organisations. Basically, the secondary data were collected from the official records and publication of Director of Agriculture, Department of Economics and Statistics, Orissa Agricultural Statistics, other publications and websites. In dealing with the issues relating to the problems, prospects and impact of agricultural marketing in Orissa and the economic development of the Jajpur district an extensive survey is made upon a few selected rural markets of the district \hyperref[b6]{[7]} {\ref [8]} {\ref [9]}.\par
V. 
\section[{Research Methodology}]{Research Methodology}\par
Data will be tested and analysed through the computer based statistical techniques like co-relations and multiple regression. Statistical tools like average, co-efficient of variation, multiple regressions, analysis of Variance (ANOVA) are applied to examine the result. The data analysis and interpretation is undertaken mostly with the help of computer based statistical empirical analysis. Independent variablesArea (F2) No. of villages (F3) Population (F4) Commodity (F5) Livestock (F6) Transportation (F7) Traders (F8) Infrastructure Facilities (F9) Income (F10) Expenditure (F11) Savings (F12) Dependent Variable -Distance from district Head Quarter (F1)\par
The linear regression technique is employed to know whether Distance from District Head Quarters is dependent on what independent variables and find out if there is a significant relationship between distance and other independent variables giving raise to the coefficient of determination value.But, in the present study all the variables are considered as dependent and independent variable and the inferences are drawn accordingly.The form of equation is given below linear model F1=C0+C2F2+C3F3+C4F4+C5F5+C6F6+C7F7+ 8F8+C9F9+C10F10+C11F11+C12F12 
\section[{Karl Pearson's Coefficient of Correlation}]{Karl Pearson's Coefficient of Correlation}\par
Correlation in statistics refers to relationship between any two, or more variables. Two variables are said to be correlated if with a change in the value of one variable there arises a change in the value of another variable. i) that there is linear relationship between the two variables; ii) that the two variables are casually related which means that one of the variables is independent and the other one is dependent; andF Mean sum o =\par
Mean sum of square of explained sum square f square of residual sum square iii) a large number of independent causes are operating in both variables so as to produce a normal distribution.\par
There are different methods of studying correlation between any two or more series. But for measuring the correlation between any two variables i.e. simple correlation, Karl Pearson's co-efficient method is used. Karl Pearson's Coefficient of Correlation (r) = The value of 'r' lies between ± 1. Positive value of 'r' indicates positive correlation between two variables, changes in both the variables take place in same direction, whereas negative values of 'r' indicates a negative correlation i.e. changes in the two variables taking place in opposite direction. A zero value of 'r' indicates that there is no association between two variables.2 2 2 2 ) ( . ) ( . ? ? ? ? ? ? ? ? ? ? Y Y N X X N Y X XY N X = 
\section[{VI.}]{VI.} 
\section[{Hypothesis}]{Hypothesis}\par
The marketing sector of perishable agriculture commodities good is a highly unorganized sector. There exists no definite system for trading of perishable commodities. The strategy evolved as the outcome of the study may not be general. The study was conducted based on the following assumptions. 1. Price realization for the perishable agriculture commodities is having a direct correlation with the unorganized nature of the farmers in the sector. 2. Hesitancy expected from the part of fruit and vegetable traders in associating with farmer markets. 3. Success rate of marketing systems in perishable agriculture commodities has a direct correlation with the transparency in the management system. 4. Participatory decision making improves the efficiency and transparency of the rural markets.\par
The scope of the study is very wide. The present study focuses on the agricultural business, agricultural marketers, and marketing channel strategy in the study area. This study will be helpful for the marketers, traders, agents, producers of agriculture, researchers and the most important the consumers. The study provides detail understanding and analysis of various aspects of agricultural distribution.\par
a) The student's t-test of the null hypothesis A t-test is any statistical hypothesis test in which the test statistic follows a Student's t distribution if the null hypothesis is supported. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. We compute the t-statistic for each Ci ,which follows tdistribution with (n-1) degrees of freedom.The null hypothesis is Ci = 0. if < t (tabulated), we accept the null hypothesis i.e. we accept that is not significant. if > t (tabulated), we reject the null hypothesis and we accept the alternative one. i.e. is statistically significant. Thus, greater the value of the stronger the evidence that Ci is statistically significant. 
\section[{VII.}]{VII.} 
\section[{Analysis of Variance Test}]{Analysis of Variance Test}\par
In the analysis, the total variations are split into explained and unexplained variation. This suggests that one can compute an analysis of variance type of table for analysis.\par
An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.The ANOVA F-test can be used to assess whether any of the treatments is on average superior, or inferior, to the others versus the null hypothesis that all four treatments yield the same mean response. 
\section[{F-statistic is computed as}]{F-statistic is computed as}\par
The null hypothesis Ho is Ci = 0 if calculated F > tabulated F with (k-1) and (n-k) degrees of freedom with chosen level of significance we reject the null hypothesis and accept that the data is significant.\par
If calculated F < tabulated F, then we accept the null hypothesis and conclude that data is not significant. 
\section[{Goodness of Fit (R 2 )}]{Goodness of Fit (R 2 )}\par
In statistics, the coefficient of determination, denoted R 2 and pronounced R squared, indicates how well data points fit a line or curve. It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes.R2 is a statistic that will give some information about the goodness of fit of a model. In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R 2 of 1 indicates that the regression line perfectly fits the data. In statistics, the two-way analysis of variance (ANOVA) test is an extension of the one-way ANOVA test that examines the influence of different categorical independent variables on one dependent variable. While the one-way ANOVA measures the significant effect of one independent variable (IV), the two-way ANOVA is used when there are more than one IV and multiple observations for each IV. The two-way ANOVA can not only determine the main effect of contributions of each IV but also identifies if there is a significant interaction effect between the IVs.  
\section[{Analysis of variance}]{Analysis of variance}n T n T j j 2 2 ? (c -1) (c -1) 
\section[{SS between columns MS residual MS between columns}]{SS between columns MS residual MS between columns}\par
Between rows treatment? ( ) ( ) n T n T i i 2 2 ? (r -1) (r -1) 
\section[{SS between rows MS residual MS between rows}]{SS between rows MS residual MS between rows} 
\section[{Residential or error}]{Residential or error}\par
Total SS -(SS between columns + SS between rows)(c-1)(r-1) (c -1) (r -1) MS residual Total ?X 2 ij - ( ) n T 2 (c, r -1)\par
Where, the total value of individual item (or their coded values as the case may be) in all the samples and call it T.\par
The collected data have been classified and tabulated according to their specific characteristics relevant for the study. Comparative method and experimental method of analysis are also to be adopted in this study by taking interviews from the various persons. However, a meaningful and valid study has been made based on the statistical reliable collected data. 
\section[{IX.}]{IX.} 
\section[{Multiple Regression Analysis}]{Multiple Regression Analysis}\par
Multiple regressionsis the extension of simple regression to take the account the effect of more than one independent variables on the dependent variables. It is a technique to investigate the effect on 'dependent variable' of several variables combine and individually. It begins with the simple correlation matrix and enters into regression of the independent variables most highly correlated with the dependent variables. This technique has been applied to analysethe determinants of agricultural markets and find out its role in the economic development of the district. 
\section[{a) Limitations of the study}]{a) Limitations of the study}\par
The study is confined with the following aspects due to paucity of time and limitations of resources.\par
1. The study is confirmed with only one district i.e.\par
Jajpur district out of 30 districts in the state, Odisha. 2. The study covers only the agricultural producers, users and tradesmen linked thereto. The problems of marketing of agricultural community are identified. 3. As the district covers almost all rural areas, so the study focuses only the marketing situations that are prevalent in these rural areas as well. 4. The study focused only the economic development in one side and neglected the socio-cultural activities of farmers in other side.\par
X. 
\section[{Agricultural Marketing System in Odisha}]{Agricultural Marketing System in Odisha}\par
An efficient agricultural marketing system is imperative for development of agriculturesector of the state. In the context of global integration of markets, the The work flow of buying and selling of paddy at the RMCs is as follows: 1. A farmer with FIC arrives at the market yard of the RMC with his produce 2. Agencies like OCSC, FCI, MARKFED, NAFED etc.arrive for procuring those produce from the farmers. 3. Empanelled Millers arrives to take the produce for processing and storage. 4. RMC agents carry out their quality checks of the produce like weight verification, grading of the produce, moisture measurement etc. 5. After the quality check is done the price is decided as per the grade of the produce and the procurement agency pay the farmer through cheque. 6. The miller produces the transit pass or the form IV while passing through the RMCs owned check gates and no market fee is charged from them.= ? = ? ? ? ? ? ? Y e Y e Y i i i i i 2 2 2 2 2 1 © 2013 Global Journals Inc. (US) 
\section[{f) Regulated Market Committee Functions}]{f) Regulated Market Committee Functions}\par
The main objective of the RMCs is to ensure payment of fair price to the agriculturist.\par
1. An RMC works as a facilitator between the farmers and the procuring agencies. It facilitates both the party by providing facilities. 2. RMC is a revenue earning body and it earns its revenue by charging 2\% of the transaction amount for providing these above mentioned facilities.  
\section[{g) Agricultural Marketing in Jajpur District}]{g) Agricultural Marketing in Jajpur District}\par
Orissa, a State in the Union of India is a classic land of paradoxes. Rich in history andgeography, the State is today the poorest one in thecountry. Jajpur District is located in the eastern region of the state. The district has an area of 2899sqkms. Physiographically, the district may be divided into two physical regions. These regions are (1) the eastern alluvial plains and (b) the north western hilly terrains. The district is washed by many rivers like the Brahmani, the Baitarani, the Birupa, the Kharasrota, the Kelua, the Budha, the Kani and the Genguti \hyperref[b18]{[22]}\hyperref[b19]{[23]} {\ref [24]}.Odisha is essentially an agricultural economy. Agriculture remains the main occupation of large majority of Odisha people. It provides livelihood support to a large section of its population. So Agricultural development is the crux of overall development of the economy of the State. But at present agriculture suffers from many problems. Both agriculture and agriculturists will develop when farmers will get fair price for the produce. That is why agricultural marketing is an important part of the agrarian sector. Agricultural Marketing System plays dual role in the economic development of the State, Odisha whose resources are primarily agriculture \hyperref[b20]{[25]} {\ref [26]}\hyperref[b21]{[27]}.\par
To understanding the agricultural marketing system of the districti.e the aspects include agricultural products produced by the farmers, their marketable surplus, types of agricultural market found in the district, marketing facilities available, problems faced by the farmers and benefits received by the consumers. Government's interference and protection of interest of producers and consumers and the role of marketing cooperatives and regulated markets with reference to two agriculturally developed blocks of the state namely Jajpur and Rasulpur have been explained to elaborate the present scenario of agricultural marketing in this district. Odisha in general and in Jajpur district in particular. The farmers are forced with distress sale to honors their debt obligation. Though the state is blessed with several rivers and availability other related facilities that are meant for growing more agricultural production and productivity, still need is inevitable to improve the economic standard of the producer farmers. Presently conducted survey reveals that most of the agripopulated areas are not well-connected with the markets or mandies consequently creating the stumbling blocks in transporting the produce from the place of production to the place of consumption. Now present construction of roads in the rural areas of the district is not sufficient to meet the requirements of better transportation. Some roads are there where are so poor which became unusable in the rainy season. The presence of superfluous middlemen brings down to the rate of return to the producers spacious market yards are not available to the farmers for demonstrating their products freely to the consumers. The products are usually displayed on the roads of NHS which creates obstacles in the movement of vehicles and causes a number of accidents. The farmers are ignorant about market information. Being ignorant, they are heavily exploded by the middlemen and financial initiatives in several ways. Besides these constraints, other challenges make them handicapped in developing their profession \hyperref[b22]{[28]}. Agricultural Knowledge Systems (AKMS), considering in particular developments in institutional frameworks, public and private roles and partnerships, regulatory frameworks conducive to innovation, the adoption of innovations and technology transfers, and the responsiveness of AKMS to broader policy objectives. The conference demonstrated a wide diversity of approaches to AKMS, with each responding to different agro-economic, social and institutional challenges, and each with a different history. Most strikingly, all of these approaches are currently evolving from a linear AKMS to more integrated innovation systems. The question is whether these developments will successfully address the challenges identified at this conference -namely, those arising at the nexus of food security and climate change. Some speakers have emphasised public approaches, while others emphasised private. Many talked about co-development and networks, including the speakers who focused on biotechnology, and several speakers mentioned the importance of local traditional knowledge. Finally, many emphasised the shift taking place from research and development (R\&D) to innovation in products and processes \hyperref[b23]{[29]}. 
\section[{Global}]{Global}\par
? Promote the development of knowledge in Science and Technology (S\&T) laboratories. ? e-Agriculture service for accelerating agricultural development and living standards of farmers. ? Promote knowledge applications in agriculture and industry. ? Promote the use of knowledge capabilities in making government an effective, transparent and accountable service provider to the citizen and promote widespread sharing of knowledge to maximise public benefit. 
\section[{i) Functional Analysis of Regulated Agricultural Markets}]{i) Functional Analysis of Regulated Agricultural Markets}\par
The performance of agricultural marketing systems in developing countries has often been poor and government intervention has not been very successful. This is the right time to explain the weak impact of many interventions in agricultural marketing by public authorities. The methodologies applied in agricultural marketing analysis are not adequate or have not been applied with sufficient rigor, thereby leading to poor understanding of the problems and opportunities and resulting in incorrect recommendations for change. Further the public agricultural marketing research methodologies provide a correct view of the market havenot been concerned enough with the interactions between marketing systems and agricultural development. Agribusiness industries face a stiff competition originating mainly from trade barrier's removal and the rapidly changing marketing environment. Therefore, certain need has been identified towards the development and proper utilization of updated market research tools and methodologies in the field of agricultural marketing. The aim of this study is to show the usefulness of multi-criteria approach in analyzing consumer's preference data and its ability to support new product development processes by agricultural firms. Several methodological issues in agricultural marketing are then presented through a state-of-the art survey. Then, the study develops a consumer-based methodology to support product development decisions where the key-role is played to determine the preference model which explains a single consumer's ranking; a decision support system summarizes the analysis on the whole set of interviewed consumers to prescribe the `ideal' profile of a new product and to simulate its penetration strategy into the market. Results from the application of the methodology to a survey data base collected from various sources are presented.\par
To know the impact of agriculturalmarketing, consumer and producerstrategy for variables such as Distance from district Head Quarter (F1), Area (F2), No. of villages (F3), Population (F4), Commodity (F5), Livestock (F6),Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) for the data collected the linear regression techniques is implemented using Statistical Package for Social Science (SPSS), which estimates the co-efficient of the linear equation involving one or more independent variables that best predicts the value of the dependent variable. a Dependent Variable: F1 Hence, from the coefficient table we understand that from F2, F3, F4, F5, F6, F7, F8, F9, F10, F11 and F12 only constant and F12 (Savings) has an impact on Distance. So, savings will be impacted when there is a change in distance. 
\section[{j) Multiple Regression Analysis in}]{j) Multiple Regression Analysis in}\par
ii. Area If Area (F2) is considered as the dependable variable and Distance (F1), No. of villages (F3), Population (F4), Commodity (F5), Livestock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) as independent variables. The linear regression technique is employed to know whether Area is dependent on what independent variables and find out if there is a significant relationship between area and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.280 and this R2 value is significant. The regression to predict Expenditure (F11) is relevant from the coefficient table. So, it is evident from the coefficient table that constant, Expenditure (F11) has significant relationship with the Area (F2). a Dependent Variable: F2 Hence, from the coefficient table we understand that from F1, F3, F4, F5, F6, F7, F8, F9, F10, F11 and F12 only constant and F11 (Expenditure) has an impact on Area. So, expenditure will be impacted when there is a change in area. 
\section[{Global Journal of Management and}]{Global Journal of Management and}\par
iii. No. of villages If No. of villages (F3) is considered as the dependable variable and Distance (F1), Area (F2), Population (F4), Commodity (F5), Livestock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) as independent variables.\par
The linear regression technique is employed to know whether No. of villages is dependent on what independent variables and find out if there is a significant relationship between no. of villages and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.553 and this R2 value is significant. Hence, from the coefficient table we understand that from F1, F2, F4, F5, F6, F7, F8, F9, F10, F11 and F12 only constant and F12 (Savings) has an impact on No. of villages. So, savings will be impacted when there is a change in no. of villages. a Dependent Variable: F4 Hence, from the coefficient table we understand that from F1, F2, F3, F5, F6, F7, F8, F9, F10, F11 and F12 constant and F11 (Expenditure) has an impact on Population followed by F6 (Livestock). So, expenditure and livestock will be impacted when there is a change in population. 
\section[{v. Commodity}]{v. Commodity}\par
If Commodity (F5) is considered as the dependable variable and Distance (F1), Area (F2), No. of villages (F3), Population (F4), Live-stock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) as independent variables.\par
The linear regression technique is employed to know whether Commodity is dependent on what independent variables and find out if there is a significant relationship between commodity and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.563 and this R2 value is significant. Hence, from the coefficient table we understand that from F1, F2, F3, F4, F6, F7, F8, F9, F10, F11 and F12 constant and F8 (Traders) has an impact on Commodity (F5). So, Commodity will be impacted when there is a change in Traders. 
\section[{vi. Livestock}]{vi. Livestock}\par
If Live-stock (F6) is considered as the dependable variable and Distance (F1), Area (F2), No. of villages (F3), Population (F4), Commodity (F5), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) as independent variables.\par
The Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F7, F8, F9, F10, F11 and F12 constant and F12 (Savings) has an impact on Livestock (F6). So, Savings will be impacted when there is a change in Livestock. a Dependent Variable: F7 Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F8, F9, F10, F11 and F12 constant and Expenditure (F11) followed by Livestock (F6) has an impact on Transportation (F7). So, Expenditure followed by livestock will be impacted when there is a change in Transportation. a Dependent Variable: F8 Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F7, F9, F10, F11 and F12 constant and Livestock (F6) followed by Expenditure (F11) has an impact on Traders (F8). So, Livestock followed by Expenditure will be impacted when there is a change in Traders.  Overall it is found that in case of regulated market the variable savings followed by expenditure and in case of unregulated market the variable income followed by expenditure has most significant contribution towards development of agricultural market for both the consumer and producer in the study area. So, financial status is the most important indicator for marketing systems in agriculturefound in the present study. 
\section[{XI.}]{XI.} 
\section[{Summary and Conclusion}]{Summary and Conclusion}\par
Market committees provide information through notice board of RMC at main market yard only and few RMCs uploading price information in AGMARKNET also. Majority of farmers depends on their peers for access of information related to marketing of produce. Many of the regulated wholesale markets have a principal market with large area and relatively better infrastructure and number of sub-yards attached to the principal market. The establishment of regulated markets has helped in creating orderly and transparent marketing conditions in primary assembling markets.\par
However, this does not mean that everything is fine in all the regulated markets of the district. The facilities created in market yards continue to be inadequate. The cleaning, grading and packaging of agril.produce before sale by the farmers have not been popularized by the market committees on a sufficient scale.The basic facilities viz., internal roads, boundary walls, electric light, loading and unloading facilities and weighing equipment's are available in more thaneighty percent of the markets. One can realign the policy set toward increasing agricultural innovation, helping producers to increase output via improved productivity,\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
   \begin{figure}[htbp]
\noindent\textbf{for} \par 
\begin{longtable}{P{0.29448818897637796\textwidth}P{0.16062992125984252\textwidth}P{0.15393700787401576\textwidth}P{0.19409448818897637\textwidth}P{0.0468503937007874\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Two-way Anova\tabcellsep \\
Source of\tabcellsep Sum of\tabcellsep Degrees of\tabcellsep Mean square (MS)\tabcellsep F-ratio\\
variation\tabcellsep squares(SS)\tabcellsep freedom\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep (d.f.)\tabcellsep \tabcellsep \\
Between columns treatment\tabcellsep ( ) ( )\tabcellsep \tabcellsep \tabcellsep \\
?\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table for}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.04501408450704225\textwidth}P{0.4879718309859155\textwidth}P{0.31701408450704227\textwidth}}
\tabcellsep \tabcellsep OSAM Board is the sole authority and controlling\\
\tabcellsep \tabcellsep body of all the RMCs. Committee works as a\\
\tabcellsep \tabcellsep facilitator between the farmers and the procuring\\
\tabcellsep \tabcellsep agencies[20-21]. The following facilities are provided in\\
\tabcellsep \tabcellsep a market yard.\\
\tabcellsep \tabcellsep 1. Market Yard\\
\tabcellsep \tabcellsep 2. Weigh Bridge\\
\tabcellsep \tabcellsep 3. Meeting of Farmer \& Agencies\\
\tabcellsep \tabcellsep 4. Godown for Storage (if not sold)\\
\tabcellsep \tabcellsep 5. Cold Storage for perishable items\\
\tabcellsep \tabcellsep d) Revenue Sources of RMCS\\
2013\tabcellsep \tabcellsep 1. RMC is a revenue earning body and it earns its revenue by charging 1\% (2\% for paddy) of the\\
Y ear\tabcellsep \tabcellsep transaction amount for providing these above mentioned facilities.\\
16\tabcellsep \tabcellsep 2. RMC also earn its revenue from the check gates, by charging 1\% of the way bill value from the traders\\
Volume XIII Issue IX Version I\tabcellsep Under the provisions of the Orissa Agricultural Produce Market Act, 1956, Regulated\tabcellsep carrying notified produces.\\
( )\tabcellsep Market Committees (RMCs) have been established in the State for regulation of buying and selling of\tabcellsep \\
Global Journal of Management and Business Research\tabcellsep agricultural produce. There are 65 R.M.Cs covering 55 Revenue Sub-Divisions of the State. The Chairman of the R.M.Cs are Collectors/ A.D.Ms/ Sub-Collectors[13-15]. b) Markets and Market Yards Across the 65 RMCs in the state at present, there are 428 market yards in the State, which include 53 Principal Market Yards, the rest being sub-market yards. Besides, there are 567 temporary market yards for Paddy Procurement. Out of 314 Blocks, 111 Blocks do not have any reulated market. Now under the award of the 13thFinance Commission, markets are being set up in these uncovered blocks. The RMCs are making efforts to upgrade the facilities in the existing market yards and the Gram Panchayat markets under their control and efforts are also being made to establish new market yards[16-19].\tabcellsep \\
\tabcellsep constituted RMC is having a nominated Committee.\tabcellsep \end{longtable} \par
  {\small\itshape [Note: of a market driven agricultural ecosystem is of significant importance. The Royal commission on Agriculture (1928) commented at length on the defects prevailing in the agricultural marketing sector and recommended that these can only be removed by the establishment of such markets that would confer immense scope for the cultivating classes in India. Accordingly, most of the States took steps forTable 1 : -VIII. Market Yards, Krushak Bazaars and for development of existing market yards in the RMCs. The OSAM Board undertakesmassive extension activities like organizing farmers' awareness campaigns and exhibitions to educate the farmers on their rights and the marketing facilities available for them[12]. a) Establishment af Regulated Market Committees (Rmcs) and Market Yards e) Work Flow of Buying and Selling at RMCS]} 
\caption{\label{tab_1}Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India) An Empirical Analysis importance}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7135845213849287\textwidth}P{0.0010386965376782077\textwidth}P{0.003462321792260692\textwidth}P{0.0020773930753564154\textwidth}P{0.0020773930753564154\textwidth}P{0.09452138492871691\textwidth}P{0.03323828920570265\textwidth}}
\multicolumn{6}{l}{c) Regulated Market Committee (RMCS) competitive price for the produce and prevent the cheating of farmers by market functionaries. 7. Issuing license to all the market functionaries including traders 8. Collecting market fees which are calculated on the basis of value of volume of a commodity bought and sold in the markets. Sometimes it may be based oncartload or truckload. 9. Disputes arising between producer seller and traders by reason of the quality of the producer, accounts and deductions of unauthorized charges are solved by the sub-committee of the market committee this avoid the legal complications and unnecessary expenditure. 10. Market charges are clearly defined and specified. 11. Market practices are regulated and undesirable activities are brought under control. 12. Correct weighment is ensured by periodical inspection and verification of scales and weights. 13. Suitable arrangements for the settlement of disputes is provided. 14. Reliable and up to date market news is made available to the farmers. 15. Suitable quality standards and standard terms for buying and selling are conveniently enforced. 16. Reliable statistics of arrivals, stocks, prices are maintained. 17. Other facilities like shades for the sale of produce, space for parking carts, drinking water facilities and cisterns of cattle, rest houses, grading and warehousing facilities are provided. 18. Open auction method is strictly followed. 19. Propaganda for agricultural improvement is more conveniently carried out. 20. Maintenance and improvement of the markets and its buildings. 21. Maintenance of standards of weights and measures. Regulated Agricultural Marketing 3. It also earn its revenue from the check gates, by charging 1\% of the way bill value from the traders 22. Payment of interests of loans.}\tabcellsep Global Journal of Management and Business Research Volume XIII Issue IX Version I Y 2013 ear ( )\\
Under\tabcellsep the\tabcellsep provisions\tabcellsep of the\tabcellsep Orissa\tabcellsep carrying notified produces.\\
\multicolumn{5}{l}{Agricultural Produce Markets Act, 1956, 65 RMCs}\tabcellsep 4. To achieve an efficient system of buying and selling\\
\multicolumn{5}{l}{have been established in the State for regulation of}\tabcellsep of agricultural commodities,\\
\multicolumn{5}{l}{purchase and sale of agricultural produce. Of these}\tabcellsep 5. To provide for regulation of agricultural produce\\
\multicolumn{5}{l}{65 RMCs, 61 RMCs are having elected Committees}\tabcellsep markets.\\
\multicolumn{5}{l}{while election in 3 RMCs is sub-judice and one newly}\tabcellsep 6. Conducting open auction or close tender method\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep for sales of agricultural produce to ensure a fair and\end{longtable} \par
 
\caption{\label{tab_2}System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.04164658634538152\textwidth}P{0.46698795180722885\textwidth}P{0.028674698795180722\textwidth}P{0.06417670682730924\textwidth}P{0.04506024096385542\textwidth}P{0.04983935742971888\textwidth}P{0.04710843373493975\textwidth}P{0.04096385542168675\textwidth}P{0.0655421686746988\textwidth}}
Volume XIII Issue IX Version I Y 2013 ear\tabcellsep \multicolumn{5}{l}{Regression: a All requested variables entered. F1, F10, F7, F4, F11, F5, F3, Variables Entered/Removed Variables Entered Variables Removed F8, F9, F6, F12 . b Dependent Variable: F2 Model Summary R R Square Adjusted R Square Std. Error Method Enter of the Estimate .529 .280 -.115 4741.54 a Predictors: (Constant), F1, F10, F7, F4, F11, F5, F3, F8, F9, F6, F12 Anova a Predictors: (Constant), F1, F10, F7, F4, F11, F5, F3, F8, F9, F6, F12 Squares Regression 38014.705 3455.882 .749 Residual 92332.170 4616.608 Total 130346.875 Mean Square F F11 3.194 .031 1.215 F12 -1.487 .021 -1.184 Regulated Agricultural Marketing Sum of F1 -4.386 15.573 -.063}\tabcellsep 1.020 -.717 -.282\tabcellsep .320 .482 .781\tabcellsep Volume XIII Issue IX Version I Y 2013 ear\\
( )\tabcellsep \multicolumn{2}{l}{b Dependent V ariable: F2}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( ) E\\
Business Research\tabcellsep Coefficients\tabcellsep Variables (Constant) F3 F4 F5 F6 F7\tabcellsep Unstandardized Coefficients B 7045.785 162.229 -4.374 -4.973 3.185 165.460\tabcellsep Std. Error 2674.937 147.397 .079 .298 .023 160.649\tabcellsep Standardized Coefficients Beta .303 -.149 -.048 1.039 .297\tabcellsep t 2.634 1.101 -.554 -.167 1.408 1.030\tabcellsep Sig. .016 .284 .586 .869 .175 .315\tabcellsep Global Journal of Management and Business Research\\
\tabcellsep \tabcellsep F8\tabcellsep -3.885\tabcellsep 6.483\tabcellsep -.245\tabcellsep -.599\tabcellsep .556\tabcellsep \\
\tabcellsep \tabcellsep F9\tabcellsep -282.435\tabcellsep 293.320\tabcellsep -.446\tabcellsep -.963\tabcellsep .347\tabcellsep \\
\tabcellsep \tabcellsep F10\tabcellsep -1.823\tabcellsep .002\tabcellsep -.403\tabcellsep -.804\tabcellsep .431\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.02783083219645293\textwidth}P{0.6557639836289222\textwidth}P{0.0246418826739427\textwidth}P{0.021163028649386083\textwidth}P{0.0739256480218281\textwidth}P{0.021742837653478855\textwidth}P{0.024931787175989085\textwidth}}
\tabcellsep F10\tabcellsep 2.531\tabcellsep .000\tabcellsep .299\tabcellsep .756\tabcellsep .458\\
\tabcellsep F11\tabcellsep -4.408\tabcellsep .000\tabcellsep -.897\tabcellsep -.953\tabcellsep .352\\
\tabcellsep F12\tabcellsep 2.336\tabcellsep .000\tabcellsep .995\tabcellsep .766\tabcellsep .453\\
\tabcellsep F1\tabcellsep -5.742\tabcellsep .023\tabcellsep -.044\tabcellsep -.250\tabcellsep .805\\
\tabcellsep F2\tabcellsep 3.520\tabcellsep .000\tabcellsep .188\tabcellsep 1.101\tabcellsep .284\\
\tabcellsep a Dependent Variable: F3\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{as independent variables.}\tabcellsep The linear regression\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{technique is employed to know whether Population is}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{dependent on what independent variables and find out if}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{there is a significant relationship between population}\\
Y 2013 ear Volume XIII Issue IX Version I ( ) Global Journal of Management and Business Research\tabcellsep \multicolumn{6}{l}{Variables Entered/Removed Variables Entered Variables Removed F2, F9, F5, F1, F4, F7, F8, F11, F10, F6, F12 . a All requested variables entered. b Dependent Variable: F3 Model Summary R R Square Adjusted R Square and other independent variables giving raise to the Method Enter coefficient of determination value. It is found that R2 iv. Population equals to 0.510 and this R2 value is significant. The If Population (F4) is considered as the regression to predict Expenditure (F11) is relevant from dependable variable and Distance (F1), Area (F2), No. the coefficient table followed by Livestock (F6). So, it is of villages (F3), Commodity (F5), Live stock (F6), evident from the coefficient table that constant, Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10), Expenditure (F11) and Savings (F12) Expenditure (F11) and Livestock (F6) has significant relationship with the Population (F4). Variables Entered/Removed Variables Entered Variables Removed Method F3, F1, F11, F2, F6, F7, F5, F8, F10, F9, F12 . Enter a All requested variables entered. b Dependent Variable: F4 Model Summary Std. Error of the Estimate .744 .553 .307 6.98 a Predictors: (Constant), F2, F9, F5, F1, F4, F7, F8, F11, F10, F6, F12 Anova Sum of Squares Mean Square F Regression 1207.161 109.742 2.249 Residual 975.714 48.786 Total 2182.875 a Predictors: (Constant), F2, F9, F5, F1, F4, F7, F8, F11, F10, F6, F12 b Dependent Variable: F3 Coefficients Variables Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) -1.446 4.562 -.317 .755 F4 1.983 .000 .361 1.827 R R Square Adjusted R Std. Error Square of the Estimate .714 .510 .241 13324.65 a Predictors: (Constant), F3, F1, F11, F2, F6, F7, F5, F8, F10, F9, F12 Anova Sum of Squares Mean Square F Regression 3699315751.922 336301431.993 1.894 Residual 3550924861.953 177546243.098 Total 7250240613.875 a Predictors: (Constant), F3, F1, F11, F2, F6, F7, F5, F8, F10, F9, F12 b Dependent Variable: F4 Coefficients Variables Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 2704.902 8703.046 .311 .759 F5 .500 .829 .142 .604 .553 .083 F6 6.317 .065 .605 .970 .344}\\
\tabcellsep F5 F7\tabcellsep 4.933 110.039\tabcellsep .000 462.618\tabcellsep .254 .058\tabcellsep 1.162 .238\tabcellsep .259 .814\\
\tabcellsep F6 F8\tabcellsep -4.541 .789\tabcellsep .000 18.381\tabcellsep -.793 .015\tabcellsep -1.358 .043\tabcellsep .190 .966\\
\tabcellsep F7 F9\tabcellsep 9.400 -218.673\tabcellsep .242 841.758\tabcellsep .090 -.101\tabcellsep .389 -.260\tabcellsep .702 .798\\
\tabcellsep F8 F10\tabcellsep 5.735 7.585\tabcellsep .010 .006\tabcellsep .193 .492\tabcellsep .600 1.214\tabcellsep .555 .239\\
\tabcellsep F9 F11\tabcellsep .455 .134\tabcellsep .430 .085\tabcellsep .384 1.501\tabcellsep 1.058 1.579\tabcellsep .303 .130\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.26800208116545265\textwidth}P{0.1654006243496358\textwidth}P{0.056607700312174825\textwidth}P{0.10260145681581687\textwidth}P{0.07075962539021852\textwidth}P{0.039802289281997916\textwidth}P{0.04864724245577523\textwidth}P{0.05572320499479708\textwidth}P{0.04245577523413111\textwidth}}
\tabcellsep \tabcellsep \multicolumn{4}{l}{Variables Entered/Removed}\\
\tabcellsep \multicolumn{3}{l}{Variables Entered}\tabcellsep \multicolumn{2}{l}{Variables}\tabcellsep Method\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Removed}\\
\tabcellsep \multicolumn{3}{l}{F4, F11, F1, F2, F7, F8, F3, F10,}\tabcellsep .\tabcellsep \tabcellsep Enter\\
\tabcellsep \multicolumn{3}{l}{F9, F6, F12}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{a All requested variables entered.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
b Dependent Variable: F5\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Model Summary}\tabcellsep \\
\tabcellsep R\tabcellsep \tabcellsep \multicolumn{2}{l}{R Square Adjusted R}\tabcellsep \multicolumn{2}{l}{Std. Error}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Square}\tabcellsep \tabcellsep of the\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Estimate}\\
\tabcellsep \multicolumn{2}{l}{.751}\tabcellsep .563\tabcellsep .323\tabcellsep \multicolumn{2}{l}{3560.86}\\
\multicolumn{5}{l}{a Predictors: (Constant), F4, F11, F1, F2, F7, F8, F3, F10, F9, F6, F12}\tabcellsep \\
Anova\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{3}{l}{Sum of Squares}\tabcellsep \multicolumn{3}{l}{Mean Square}\tabcellsep F\\
\multicolumn{2}{l}{Regression}\tabcellsep \multicolumn{2}{l}{327342006.671}\tabcellsep \multicolumn{3}{l}{29758364.243}\tabcellsep 2.347\\
\multicolumn{2}{l}{Residual}\tabcellsep \multicolumn{2}{l}{253594295.329}\tabcellsep \multicolumn{3}{l}{12679714.766}\\
\tabcellsep Total\tabcellsep \multicolumn{2}{l}{580936302.000}\tabcellsep \tabcellsep \\
\multicolumn{5}{l}{a Predictors: (Constant), F4, F11, F1, F2, F7, F8, F3, F10, F9, F6, F12}\tabcellsep \\
b Dependent Variable: F5\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Coefficients\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Variables\tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep \tabcellsep \multicolumn{3}{l}{Standardized}\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep \tabcellsep \multicolumn{3}{l}{Coefficients}\\
\tabcellsep B\tabcellsep \tabcellsep Std. Error\tabcellsep \tabcellsep \tabcellsep Beta\\
(Constant)\tabcellsep \multicolumn{2}{l}{-2841.264}\tabcellsep 2243.163\tabcellsep \tabcellsep \tabcellsep -1.267\tabcellsep .220\\
F6\tabcellsep -5.195\tabcellsep \tabcellsep .018\tabcellsep \tabcellsep \multicolumn{2}{l}{-.176}\tabcellsep -.292\tabcellsep .773\\
F7\tabcellsep 124.209\tabcellsep \tabcellsep 120.648\tabcellsep \tabcellsep \tabcellsep .231\tabcellsep 1.030\tabcellsep .316\\
F8\tabcellsep 6.668\tabcellsep \tabcellsep 4.681\tabcellsep \tabcellsep \tabcellsep .436\tabcellsep 1.425\tabcellsep .170\\
F9\tabcellsep 120.636\tabcellsep \tabcellsep 223.709\tabcellsep \tabcellsep \tabcellsep .198\tabcellsep .539\tabcellsep .596\\
F10\tabcellsep -8.824\tabcellsep \tabcellsep .002\tabcellsep \tabcellsep \multicolumn{2}{l}{-.202}\tabcellsep -.513\tabcellsep .613\\
F11\tabcellsep 7.635\tabcellsep \tabcellsep .024\tabcellsep \tabcellsep \tabcellsep .301\tabcellsep .317\tabcellsep .754\\
F12\tabcellsep -8.009\tabcellsep \tabcellsep .016\tabcellsep \tabcellsep \multicolumn{2}{l}{-.661}\tabcellsep -.511\tabcellsep .615\\
F1\tabcellsep 12.297\tabcellsep \tabcellsep 11.391\tabcellsep \tabcellsep \tabcellsep .184\tabcellsep 1.079\tabcellsep .293\\
F2\tabcellsep -2.804\tabcellsep \tabcellsep .168\tabcellsep \tabcellsep \multicolumn{2}{l}{-.029}\tabcellsep -.167\tabcellsep .869\\
F3\tabcellsep 128.214\tabcellsep \tabcellsep 110.333\tabcellsep \tabcellsep \tabcellsep .249\tabcellsep 1.162\tabcellsep .259\\
F4\tabcellsep 3.574\tabcellsep \tabcellsep .059\tabcellsep \tabcellsep \tabcellsep .126\tabcellsep .604\tabcellsep .553\end{longtable} \par
 
\caption{\label{tab_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.5066024268379729\textwidth}P{0.026695217701641683\textwidth}P{0.12376873661670235\textwidth}P{0.03640256959314775\textwidth}P{0.01516773733047823\textwidth}P{0.051570306923625985\textwidth}P{0.05703069236259815\textwidth}P{0.032762312633832974\textwidth}}
\multicolumn{3}{l}{Income (F10), Expenditure (F11) and Savings (F12) as}\tabcellsep \\
\multicolumn{5}{l}{independent variables. Variables Entered/Removed}\\
\tabcellsep \multicolumn{2}{l}{Variables Entered}\tabcellsep \multicolumn{2}{l}{Variables}\tabcellsep Method\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Removed}\\
\multicolumn{3}{l}{F6, F2, F5, F1, F11, F4, F3, F8,}\tabcellsep .\tabcellsep Enter\\
a All requested variables entered. b Dependent Variable: F7\tabcellsep \multicolumn{3}{l}{F9, F10, F12 Model Summary}\tabcellsep 2013 ear Y\\
\tabcellsep R\tabcellsep \multicolumn{2}{l}{R Square Adjusted R}\tabcellsep Std. Error\\
\multicolumn{5}{l}{Square .363 a Predictors: (Constant), F6, F2, F5, F1, F11, F4, F3, F8, F9, F10, F12 .767 .589 Anova Sum of Squares Mean of the Estimate 6.43 Square Regression 1184.964 107.724 Residual 827.255 41.363 Total 2012.219}\tabcellsep F 2.604\tabcellsep Volume XIII Issue IX Version I\\
\multicolumn{5}{l}{a Predictors: (Constant), F6, F2, F5, F1, F11, F4, F3, F8, F9, F10, F12 b Dependent Variable: F7 Coefficients Variables Unstandardized Coefficients Standardized Coefficients B Std. Error Beta (Constant) 3.343 4.144 F8 -1.0 .008 -.499 F9 .762 .370 .670 F10 7.441 .000 .092 F11 4.296 .000 .911 F12 -3.622 .000 -1.607 F1 -2.317 .021 -.186 F2 3.044 .000 .170 F3 7.969 .205 .083 F4 2.564 .000 .049 F5 4.052 .000 .218 F6 4.490 .000 .816}\tabcellsep t .807 -1.715 2.060 .239 1.011 -1.326 -1.129 1.030 .389 .238 1.030 1.469\tabcellsep Sig. .429 .102 .053 .814 .324 .200 .272 .315 .702 .814 .316 .157\tabcellsep ( ) Global Journal of Management and Business Research\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{vii. Transportation}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{If Transportation (F7) is considered as the}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{dependable variable and Distance (F1), Area (F2), No.}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{of villages (F3), Population (F4), Commodity (F5), Live-}\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{stock (F6), Traders (F8), Infrastructure Facilities (F9),}\end{longtable} \par
 
\caption{\label{tab_9}Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.05638673253352153\textwidth}P{0.25973888496824277\textwidth}P{0.03359209597741708\textwidth}P{0.10077628793225124\textwidth}P{0.0647847565278758\textwidth}P{0.23334509527170078\textwidth}P{0.005998588567395907\textwidth}P{0.010197600564573042\textwidth}P{0.046788990825688076\textwidth}P{0.038390966831333806\textwidth}}
\tabcellsep \multicolumn{6}{l}{independent variables. Variables Entered/Removed}\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Variables Entered}\tabcellsep \multicolumn{2}{l}{Variables}\tabcellsep \multicolumn{2}{l}{Method}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Removed}\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{F7, F11, F1, F4, F2, F6, F5, F3,}\tabcellsep .\tabcellsep \tabcellsep \multicolumn{2}{l}{Enter}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{F10, F9, F12}\tabcellsep \tabcellsep \tabcellsep \\
Y 2013 ear\tabcellsep \multicolumn{3}{l}{a All requested variables entered. b Dependent Variable: F8}\tabcellsep \multicolumn{2}{l}{Model Summary}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep R\tabcellsep \multicolumn{2}{l}{R Square Adjusted R}\tabcellsep \multicolumn{3}{l}{Std. Error}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Square\tabcellsep \tabcellsep of the\\
Volume XIII Issue IX Version I\tabcellsep \multicolumn{7}{l}{Estimate 162.09 Sum of Squares Mean Square .788 .672 a Predictors: (Constant), F7, F11, F1, F4, F2, F6, F5, F3, F10, F9, F12 .888 Anova Regression 1956824.002 177893.091 Residual 525435.967 26271.798 Total 2482259.969 a Predictors: (Constant), F7, F11, F1, F4, F2, F6, F5, F3, F10, F9, F12 b Dependent Variable: F8}\tabcellsep F 6.771\\
( ) Global Journal of Management and Business Research\tabcellsep Coefficients\tabcellsep Variables (Constant) F9 F10 F11 F12 F1 F2 F3 F4 F5 F6 F7\tabcellsep Unstandardized Coefficients B 215.303 1.620 5.107 1.927 -1.206 -.359 -4.539 3.088 1.168 1.382 2.360 -9.022\tabcellsep Std. Error 94.574 10.250 .000 .001 .001 .527 .008 5.143 .003 .010 .001 5.262\tabcellsep \multicolumn{3}{l}{viii. Traders Standardized Coefficients Beta .041 .179 1.164 -1.524 -.082 -.072 .092 .006 .211 1.221 -.257}\tabcellsep t 2.277 .158 .655 1.909 -1.812 -.680 -.599 .600 .043 1.425 3.833 -1.715\tabcellsep Sig. .034 .876 .520 .071 .085 .504 .556 .555 .966 .170 .001 .102\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{If Traders (F8) is considered as the dependable}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{variable and Distance (F1), Area (F2), No. of villages}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{(F3), Population (F4), Commodity (F5), Live-stock (F6),}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Transportation (F7), Infrastructure Facilities (F9), Income}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{(F10), Expenditure (F11) and Savings (F12) as}\end{longtable} \par
 
\caption{\label{tab_10}Regulated AgriculturalMarketing System for Economic Development of Jajpur district in Orissa(India) - An Empirical Analysis}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
2013\\
ear\\
Y\\
Volume XIII Issue IX Version I\\
( )\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_11}Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis is}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
2013\\
ear\\
Y\\
Volume XIII Issue IX Version I\\
( )\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_12}Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.05272556390977443\textwidth}P{0.10225563909774435\textwidth}P{0.2093045112781955\textwidth}P{0.10704887218045113\textwidth}P{0.1581766917293233\textwidth}P{0.11823308270676691\textwidth}P{0.10225563909774435\textwidth}}
F11\tabcellsep -.195\tabcellsep \multicolumn{3}{l}{Variables Entered/Removed .437** .154 .005}\tabcellsep \multicolumn{2}{l}{.108 .311**}\\
F12\tabcellsep .177\tabcellsep \multicolumn{2}{l}{Variables Entered .130 .218*}\tabcellsep \multicolumn{3}{l}{Variables Method .002 .354** .300**}\\
\tabcellsep F7\tabcellsep F8\tabcellsep F9\tabcellsep F10\tabcellsep F11\tabcellsep F12\\
F1\tabcellsep -.322\tabcellsep -.107\tabcellsep .031\tabcellsep -.130\tabcellsep -.195\tabcellsep .177\\
F2\tabcellsep .134\tabcellsep -.048\tabcellsep .255*\tabcellsep .393**\tabcellsep .437**\tabcellsep .130\\
F3\tabcellsep -.032\tabcellsep -.014\tabcellsep .312**\tabcellsep .189\tabcellsep .154\tabcellsep .218*\\
F4\tabcellsep .154\tabcellsep -.041\tabcellsep .014\tabcellsep .143\tabcellsep .005\tabcellsep .002\\
F5\tabcellsep .061\tabcellsep .225*\tabcellsep .350**\tabcellsep .211*\tabcellsep .108\tabcellsep .354**\\
F6\tabcellsep .142\tabcellsep .437**\tabcellsep .717**\tabcellsep .498**\tabcellsep .311**\tabcellsep .300**\\
F7\tabcellsep 1.000\tabcellsep . 104\tabcellsep -.060\tabcellsep .027\tabcellsep .096\tabcellsep -.076\\
F8\tabcellsep .104\tabcellsep 1.000\tabcellsep .327**\tabcellsep .187\tabcellsep .020\tabcellsep .136\\
F9\tabcellsep -.060\tabcellsep .327**\tabcellsep 1.000\tabcellsep .652**\tabcellsep .428**\tabcellsep .583**\\
F10\tabcellsep .027\tabcellsep .187\tabcellsep .652**\tabcellsep 1.000\tabcellsep .872**\tabcellsep .662**\\
F11\tabcellsep .096\tabcellsep .020\tabcellsep .428**\tabcellsep .872**\tabcellsep 1.000\tabcellsep .345**\\
F12\tabcellsep -.076\tabcellsep .136\tabcellsep .583**\tabcellsep .662**\tabcellsep .345**\tabcellsep 1.000\end{longtable} \par
  {\small\itshape [Note: * *]} 
\caption{\label{tab_13}Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India)- An Empirical Analysis}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{Regulated Agricultural Marketing System for Economic Development of Jajpur district in Orissa(India)-An Empirical Analysis} 			\footnote{regression to predict Savings (F12) is relevant from the coefficient table. So, it is evident from the coefficient table that constant, Savings (F12) has significant relationship with the Income (F10).} 		 		\backmatter  			 \par
Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F7, F8, F10, F11 and F12 constant and Savings (F12) has an impact on Infrastructure facilities (F9). So, savings will be impacted when there is a change in infrastructure facilities.\par
x. Income If Income (F10) is considered as the dependable variable and Distance (F1), Area (F2), No. of villages (F3), Population (F4), Commodity (F5), Livestock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Expenditure (F11) and Savings (F12) as independent variables. The linear regression technique is employed to know whether Income is dependent on what independent variables and find out if there is a significant relationship between Income and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.861 and this R2 value is significant. Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F7, F8, F9, F11 and F12 constant and Savings (F12) has an impact on Income (F10). So, savings will be impacted when there is a change in income. 
\subsection[{xi. Expenditure}]{xi. Expenditure}\par
If Expenditure (F11) is considered as the dependable variable and Distance (F1), Area (F2), No. of villages (F3), Population (F4), Commodity (F5), Livestock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10) and Savings (F12) as independent variables. The linear regression technique is employed to know whether Expenditure is dependent on what independent variables and find out if there is a significant relationship between Expenditure and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.976 and this R2 value is significant. The regression to predict Savings (F12) is relevant from the coefficient table. So, it is evident from the coefficient table that constant, Savings (F12) has significant relationship with the Expenditure (F11 Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F7, F8, F9, F11 and F12 constant and Savings (F12) has an impact on Income (F10). So, savings will be impacted when there is a change in expenditure. 
\subsection[{xii. Savings}]{xii. Savings}\par
If Savings (F12) is considered as the dependable variable and Distance (F1), Area (F2), No. of villages (F3), Population (F4), Commodity (F5), Livestock (F6), Transportation (F7), Traders (F8), Infrastructure Facilities (F9), Income (F10) and Expenditure (F11) as independent variables. The linear regression technique is employed to know whether Savings is dependent on what independent variables and find out if there is a significant relationship between Savings and other independent variables giving raise to the coefficient of determination value. It is found that R2 equals to 0.987 and this R2 value is significant. The regression to predict Expenditure (F11) is relevant from the coefficient table. So, it is evident from the coefficient table that constant, Expenditure (F11) has significant relationship with the Savings (F12 a Dependent Variable: F12 Hence, from the coefficient table we understand that from F1, F2, F3, F4, F5, F6, F7, F8, F9, F10 and F11 constant and Expenditure (F11) has an impact on Savings (F12). So, expenditure will be impacted when there is a change in savings.  
\subsection[{Consumer and Producer Correlation Coefficient}]{Consumer and Producer Correlation Coefficient}			 			  				\begin{bibitemlist}{1}
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\end{document}
