Introduction The study of the long-run common trends between macroeconomic and financial time series data is an imperative econometric analysis, because it assist economist to determine the correlation between various economic variables, which leads to forecasting and rational decisions made by individuals, firms and the government on issues that affects the economy of a country and as such the world economy. The analysis of the integration of economic and financial time series data by Christopher Sims (1980) suggests the Vector Auto-Regression (VAR) model as a credible methodology for this purpose. The VAR is an n-variable linear model in which each variable is in turn explained by its own lagged values, plus current and past values of the remaining n-1 variables. This means more than one variable can be analysed at the same time to find out the relationship that exist between them. Therefore the vector regression form:

C:Userst01mnn0DesktopCapture.PNG (1)

where i are (n x n) coefficient matrices and t is an (n x 1) unobservable zero mean white noise vector process (serially uncorrelated or independent) with time invariant covariance matrix âˆ‘. To solve this, it can be treated like a multivariate least square problem:

C:Userst01mnn0DesktopCapture.1PNG.PNG (2)

where Y is the matrix of the dependent variables in the form of columns representing each variable.

In a VAR analysis, it is important that the variables are stationary I(0)-meaning no unit root exist in the model-so as to support the assumption that the statistical characteristics of the data will behave the same way in the future as it has in the past. However, it is suggested that differencing to create stationarity should not be encouraged, because it is argued that the aim of VAR analysis is solely to examine the correlation between the variables, and the differing will eliminate information on any long-run relationships between the variables, (Brooks, 2008).

Economic and financial time series data, are usually known to have a common stochastic trend, this means they are correlated in the sense that they are known to linearly follow a trend on the long-run. A set of such series are considered as co-integrated when it contains one unit root I(1) and a linear combination of them is stationary. It was first suggested by Granger (1981) that a vector of time series that become a stationary process when differenced, can also have a linear combination that has a stationary process without differencing, it can then be said that such variables are co-integrated, which leads to the question of how much differencing should be carried out on the variables in regards to the combination of the time series considered. It has been identified that when all the variables are differenced from their univariate properties appropriately, then the model no longer has a multi-variate linear time series representation with an invertible moving average. In such a case the model can be said to have been over-differenced. Engle and Granger (1987) pointed out that a co-integrated structure can be represented in an error correction model which includes both the stationary and non-stationary characteristics of macroeconomic time series, that is, a set non-stationary series combinations that have a common economic factor that affects them in the same way, so that they exist a common trend between them and as such will always move linearly together in the long-run even if they drift apart from each other in the short-run. These factors could be inflation, interest rates and/or economic policies. The error correction model provides a methodology that can be used to estimate, forecast and test co-integration. The Engle and Granger method also known as the two-step technique is considered not to be credible enough due to some problems that involved in its procedure. This is evident in the analysis carried out by Xu (2005) which was to check the efficiency of the two-step method used by Lattau and Ludvigson (2001) and the Vector Error Correction Model (VECM) method to check for the co-movement in both German and US data. It was concluded that the VECM is more appropriate method to study the effect of consumption-wealth ratio (cay) on stock return and the excess returns in both data set significantly.

The aim of this paper is to use the VECM to analyse for co-integration between three European stock markets, namely; UK, Germany and French stock markets, in an attempt to replicate to an extent the analysis carried out by Pascual (2003) in his paper “Assessing European stock markets (co)integration” using the Johansen test. However, although Pascual (2003) uses the quarterly data of the European stock market indices from 1960 to 1999, this paper will use a sample size of 192 observations from 1963 to 2010 of the same stock market indices, this is due to data availability issues. Also, this paper will concentrate mainly on using the Johansen test to measure the co-movement of the markets, comparing co-integration results at different point in time to find out if there exists evidence of an increasing convergence of the European stock markets as the observations increases. The following section is the review of literatures on various analysis undertaken to investigate for co-integration using VECM, next is the description of the methodology that will be used in this papers’ analysis, followed by presentation and interpretation of results.

Literature Reviews Financial market integration has been a subject of extensive research in economic literatures for a long time, with the aim of investigating the evidence of the co-integration relationship between national stock indices by studying the long-run co-movements of these markets. According to Corhay et al (1993), this interest is spurred from the “increase in the flow of capital across national boundaries, possible gains from international diversification and the existence of lead-lag interrelationships among stock exchanges”. However, different methods have been used and improved upon with time. Pascual (2003) attempts to prove that an increase of convergence between the stock indices of the selected European stock markets should not be considered as an accurate inference from the recursive approach proposed by Rangvid (2001). In his opinion the results from the Rangvid (2001) analysis could be misleading because an increase in the convergence of the European markets could be interpreted to be as a result of the increase in the power of the Johansen test as the sample size increases from 20 to 156 observations. So therefore, it can be said that exist no evidence of an increasing co-integration. He then suggested an alternative method to check for increasing stock market integration. He proposes that the error correction term (ECT) should be estimated as it can reflect the speed of adjustment to deviations from the long-run co-integration relationship. A higher value of the coefficient of the ECT, could be interpreted as a higher level of the stock market integration, as the sample increases.

Corhay et al (1993), in their analysis recognises that the best approach to analyse stock prices when the variables involved are non-stationary is the use of the co-integration concept or the common stochastic trends, which suggest that various non-stationary variables do linearly move together in the long-run. It is in their opinion that since it is expected that the stock markets of two or more European countries are subject to a common market trend, then it can be said that the markets are co-integrated. Their analysis involved 389 biweekly observations, that is, from the 1 March 1975 to 30 September 1991, of stock price indices of five major European stock markets (Germany, France, Italy, UK and the Netherlands). Using the VECM approach that would be used later on in this paper, which was proposed by Johansen (1988), and Johansen and Juselius (1990) which is a maximum likelihood approach to estimate and test the number of con-integration in VAR model. In their conclusion they found evidence that reveals that they exists some long-run stochastic trends between several European stock market indices, although it was also discovered that the Italian stock prices seem not to influence this long-run trend.

Pukthuanthong and Roll (2009) in their study proposes an alternative measure of the integration of global markets. They suggest using empirically the explanatory power of multi-factor model to investigate the increasing integration of global markets as the correlation of countries market indexes is considered a poor measure. They explain that unless the same global factors affects for instance two countries indexes at the same proportion, their correlation would be imperfect even if the global factors explain the return of the indexes in both countries 100%. They observed that they seem to be an increasing co-integration between the 17 large countries over time, pointing out that simple correlation did not give an efficient result, because it failed to reveal the full extent of integration of the countries indexes over the past 30 years.

The reason for the interest by economic analyst and economic policy makers in the relationship between stock markets and their convergence could be due to the investigation of whether there is a possibility of gains from international diversification, most especially in the perspective of an investor, for instance, in the case where there exist a long-run linear common trend between national stock markets, then the possibility of gaining from international diversification in the long run is less likely. Fraser and Oyefeso (2005) in their study investigate the long-run convergence between U.S., UK and seven European stock markets. From the Johansen multivariate co-integration tests conducted which was used on a sample of monthly data over the period from 1974 to 2001of the stock market price indices of a selected set of European countries including the UK and U.S.; France, Denmark, Belgium, Germany, Italy, Sweden and Spain, shows that they exists a long-run relationship between the stock markets due to the presence of a single common stochastic trend. The suggested inference from their analysis confirms that stock markets examined are completely correlated in the long-run or the future. It was also noted that the results obtained from their investigation shows a much more degree of integration than those obtained by Corhay et al. (1993) carried out on a specified set of European markets, in their opinion, this might be as a result of the extended time period. Other paper that have supported the view that the main stock markets of the world have converged over the long-run includes that of Kasa (1992), where the observation sample are from the monthly and quarterly data of equity markets of U.S., Japan, Germany, England and Canada from 1974 to mid-1990. In Taylor and Tonks (1989) they investigated the effect of the abolition of the UK exchange control on the degree of integration of the UK and overseas stock markets, using the Engle and Granger (1987) two steps technique to check for co-integration on time series data. Their results show evidence that conforms to that obtained from the previously mentioned co-integration analysis. In this case, with the abolition of the exchange control, the UK stock exchange has become co-integrated with that of Japan, Germany and the Netherlands, in their opinion this might have be due to the fact that since the capital control was now relaxed and as such the unexploited arbitrage opportunities have been utilized.

Syriopoulos (2004) investigates the existence of short and long-run correlation among selected major developed stock markets; Germany and the US and emerging European stock markets; Poland, Hungary, Czech Republic and Slovakia. The VECM technique was used and it was inferred that there exists co-integration relationship between the markets. It was in the authors’ opinion that domestic and external forces, which may be referred to as macroeconomic forces, affects the stock markets behaviours, which in turn leads to the long-run equilibrium, it was also observed that there exists more degree of correlation between the individual European markets and the developed markets in comparison with their fellow emerging markets. This implies that the investment strategy of international diversification of risk in order to create an efficient market portfolio return may be limited for investors interested in utilizing this investment strategy.

In Karolyi and Stulz (1996) they investigate the components of cross-country shock return co-movements. U.S and Japan shares returns which are traded in the United States were studied to find out whether macroeconomic announcements and interest rates creates shocks that affects the co-movements between the U.S and Japanese share returns. From the results obtained from the VECM empirical method, it was inferred that these macroeconomic factors do not affect the co-movements and that covariance and correlations in the markets are high when they highly volatile. In their opinion, which is similar to Syriopoulos (2004), this means that international diversification as an investment strategy to spread out risk might not be as effective as expected , as their analysis shows that diversification in this case does not provide enough cover against large shocks to national indices as one might have expected. It was also suggested by Karolyi and Stulz (1996) the covariances between countries are not constant, because they change over time and can be forecasted.

The question of what could be the reason for the increase in the co-integration in the stock markets arises. What are the macroeconomic or global factors that have led to the co-movement of the stock market indices of emerging and developed countries? Yang et al (2003) study of the effect of the establishment of the Economic and Monetary Union (EMU) on the short and long-run integration among eleven European stock markets and US stock market. Their results were similar to that obtained by Taylor and Tonks (1989) and Corhay et al (1993). It was in that opinion that modern information technology and merger of stock exchanges in Europe may be the factor that has increased the co-integration among European stock markets.

Furthermore, Ioannidis et al (2006) in using the methodology proposed by Lettau and Ludvigson (2001), which is the two-step method, examines three countries; Australia, UK and Canada. They confirmed the results from the Lettau and Ludvigson (2001) analysis that suggest that the lagged co-integration variable (cay) is a significant predictor of the expected return or excess return of the stock markets of the specified countries, just as the case in U.S. Although, Xu (2005) uses the VECM to investigate the relationship between the consumption-wealth ratio (cay) on German stock returns. The purpose of Xu (2005) analysis was to compare the efficiency of the methodology proposed by Lettau and Ludvigson (2001) and the VECM using German and U.S. data, and it was concluded that the VECM is a more appropriate method to study the effect of cay on stock returns and excess returns in both data set significantly.

It may then be said that cay might be regarded a macroeconomic factor that determines the linear trend of stock market returns in the long-run, since there are evidence that they exist a correlation between these variables and the financial markets returns. With this evidence, the stock market returns could be predictable by business cycle at rotational frequencies in the long-run.

Methodology The methodology that would be used is the Vector Error Correction Model (VECM) which has been used most frequently in the analysis of economic time series data. Engle and Granger (1987) elaborate on the fundamental of the co-integration aspect. In this paper, the co-integration analysis in the framework of vector autoregressive model (VAR) as proposed by Johansen (1988), and Johansen and Juselius (1990) would be used.

The following is a statistical explanation of the VECM analysis using the Johansen technique as denoted by Brooks (2008). In order to use the Johansen approach, a VAR with k lags containing a set of g variables (g â‰¥ 2) which are assumed to be I(1) and cointegrated, would have to be converted into a vector error correction model (VECM), such that the set up:

yt = Î²1 ytâˆ’1 Î²2 ytâˆ’2 · · · Î²k ytâˆ’k ut

g Ã- 1 g Ã- g g Ã- 1 g Ã- g g Ã- 1 g Ã- g g Ã- 1 g Ã- 1 (3)

is transformed to a vector error correction model (VECM) as below:

âˆ†yt = âˆytâˆ’k Ð“1âˆ†ytâˆ’1 Ð“2âˆ†ytâˆ’2 · · · Ð“kâˆ’1âˆ†ytâˆ’(kâˆ’1) ut (4)

where âˆ= () – Ig and Ð“i = ( – Ig . From the above VAR equation the g variables are in the first differenced form on the left hand side and on the right hand side the k-1 are the lags of the dependent variables in their differenced form, each contains a Ð“ coefficient matrix that accompanies them.

The matrix ÐŸ in the Johansen test can represent the long-run coefficient matrix, since all the âˆ†ytâˆ’i will be zero and the error term. ut will be set to their expected value of zero will leave âˆytâˆ’k = 0, in equilibrium. The rank of the ÐŸ matrix from its eigenvalue is used to calculate the number of co-integration between the ys. The eigenvalues, which are the number of its characteristic roots that are different from zero equals to the rank of a matrix. The symbol Î»i denotes the eigenvalues, which are set in ascending order as thus; Î»1 â‰¥ Î»2 â‰¥ . . . â‰¥ Î»g. In the case where the eigenvalues ( Î»s) are roots they have to be less than 1in absolute value and positive, and Î»1 will be closest to 1 which is the largest, while Î»g will be closest to 0 which is the smallest. When the analysed variables are not co-integrated, the rank of the matrix ÐŸ will not be different from zero considerably, such that Î»i â‰ˆ 0 âˆ€ i.

In a Johansen test, there are two test statistics that are used to co-integration analysis, they are in the form below:

Î»trace(r ) = âˆ’ Ti) (4)

(Î»trace = 0 when all the Î»i = 0, for i = 1, . . . , g.)

and

Î»max(r, r 1) = âˆ’ T ln(1 âˆ’r 1) (5)

where r is the represents the number of co-integrating vectors under the null hypothesis and i represents the estimated value for the i-th eigenvalue from the matrix ÐŸ. In the Î»trace, which is a joint test has a null hypothesis where the number of co-integrating vectors is less than or equal to r against an alternative hypothesis that there are more than r. In the Î»max tests a separate test is conducted on each eigenvalue with a null hypothesis that is the number of co-integrating vectors is r and an alternative hypothesis of r 1. The trace test starts with p eigenvalues, and then in succession the largest is removed. Every eigenvalue has with it an attached different co-integration vector, which is known as the eigenvectors. A significantly non-zero eigenvalue shows a significant co-integration vector.

The critical values used for the two test statistics depends on the value of the g – r, the number of non-stationary elements and how constants are included in each of the equations. When the critical value is less than the test statistics, reject the null hypothesis that there are r co-integrating vectors in support of the alternative hypothesis (r 1 for the Î»trace test or more than r for the Î»max test). The test is conducted in a sequence and under the null, r = 0, 1, . . . , g – 1 so that the hypotheses for Î»max can be represented as below as:

H0 : r = 0 versus H1 : 0 < r â‰¤ g

H0 : r = 1 versus H1 : 1 < r â‰¤ g

H0 : r = 2 versus H1 : 2 < r â‰¤ g

. . . . . . H0 : r = g âˆ’ 1 versus H1 : r = g

From the above, the first test means the null hypothesis of no presence of co-integrating vectors, therefore the corresponding ÐŸ matrix have a 0 rank. In the case where the null hypothesis (H0: r = 0) is rejected, then the null that there is one co-integrating vector (H0: r = 1) is tested and the process continues, and as such the value of r is continually increased until the null hypothesis is not rejected. The matrix ÐŸ can never be at full rank (g) as this would mean that yt is stationary. In the case where the matrix ÐŸ has 0 rank, then by correspondence to the univariate case, âˆ†yt depends only in âˆ†yt âˆ’ j and not on yt – 1, which will result to no long-run relationship between the elements of yt – 1, which in turn means no co-integration. For instance, in 1< rank (ÐŸ) < g, there are r co-integrating vectors. The matrix ÐŸ is then characterised as the product of two matrices, Î± and Î²', of the dimension (g Ã- r ) and (r Ã- g), respectively, that is,

ÐŸ = Î±Î²’ (6)

where matrix Î² denotes the co-integrating vectors, while Î± , which is known as the adjustment parameter, gives the amount of each co-integrating vector associated with each equation of the vector error correction model.

In the following section the VECM approach using the Johansen technique as explained, will be carried out on the selected three European stock markets; UK, France and Germany to investigate the possibility of an increasing market co-integration, using to an extent the recursive approach done by Pascual (2003) which is similar to that done by Rangvid (2001). The Johansen approach is then applied to the vector error correction model;

âˆ†xt = A âˆ0xtâˆ’1 i âˆ†xtâˆ’1 ut (7)

here x represents the vector containing the logarithm value of the stock market indices for the selected European countries. A larger number of the significant co-integrating vectors will be observed as time goes on if the markets are converging.

Data The data used were used to investigate for co-integration are the quarterly data of the European (UK, Germany and France) stock market indices from 1963 to 2010 which results to a total sample size of 192 observations obtained from DATASTREAM. The reason for starting this analysis from 1963 instead of 1960 as carried out by Pascual (2003) is due to data availability problems. Starting with a sample of 20 quarters from 1960:Q1 to 1964:Q4 for three European stock indices is estimated recursively by adding one extra observation at a time up to 2010:Q4. In Appendix 1, it can be observed by eye-balling the data, that as more observations are added the lines representing each variable seem to draw closer to each other and have an upward trend. According to Pascual (2003), the upward trend can be attributed to two reasons. Firstly, is the number of existing stochastic trends conducting the three dimensional systems are decreasing with time as markets become increasingly integrated. Secondly, as the observations increase from 20 to 156 the trace statistics merge to the long run values. This may be interpreted as the existence of cointegration between the variables, although the necessary analysis must be undertaken to justify this assumption. In the result representation section, four different lag windows, corresponding to 20, 60, 100, 140 and 192 observations, are analyzed.

Results Presentation The first step in the VECM analysis is to check for stationarity in the variables. Unit Root test was carried out on the log of the variables using Augmented Dickey-Fuller (ADF), Philips-Perron (PP) and Kwiatkowski-Philips-Schmidt-Shin (KPSS) test. The results are presented below:

Unit Root/Stationary Test Test Type

UK G F Critical Value

-0.804

-0.420

-0.568

ADF 1% Level

(-3.464)

Fail

Fail

Fail

5% Level

(-2.876)

Fail

Fail

Fail

10% Level

(-2.574)

Fail

Fail

Fail

Critical Value

-0.823

-0.473

-0.434

PP 1% Level

(-3.464)

Fail

Fail

Fail

5% Level

(-2.876)

Fail

Fail

Fail

10% Level

(-2.574)

Fail

Fail

Fail

Critical Value

1.650

1.604

1.615

KPSS 1% Level

0.739

Reject

Reject

Reject

5% Level

0.463

Reject

Reject

Reject

10% Level

0.347

Reject

Reject

Reject

Conclusion I(I) I(I) I(I) Table 1: Unit root results

From the above table, UK, G and F represents United Kingdom, Germany and France respectively, they denote the log of the European stock market. From the above results one can infer that the variables are I(1), meaning there exist unit roots and therefore the variables are non-stationary. These results can be illustrated in a unit root graph as below:

Figure 1: Unit Root graph

Since, one of the blue points touch the circle, we can conclude that the variables are non-stationary. The next step will be to specify the optimal lag. The below table contains the lag structure of 20, 60, 100 and 140 observations. The optimal lag is obtained when the Akaike criterion has minimum value. The Akaike Information Criterion is appropriate for this analysis since the ample size is quite small.

Akaike Information Criterion for VECM with lag 2 to lag 10 Lag

Number of Observations

20

60

100

140

2

-7.003961 -5.743871 -5.612677 -6.048935 3

-5.538380

-5.503853

-5.965187

4

-5.393658

-5.421785

-5.922316

5

-5.359694

-5.347622

-5.832997

6

-5.167633

-5.172125

-5.773959

7

-5.206056

-5.169274

-5.730607

8

-5.260565

-5.109051

-5.632572

9

-5.083367

-4.979757

-5.535563

10

-5.136492

-4.869142

-5.514630

Table 2: Akaike Information Criterion

From the above table, comparing the information criteria shows that VAR (1, 2) gives the smallest information criteria for all the different categories of observations and so it is the best linear unbiased estimation. For 20 observations only VAR (1, 2) was obtainable because it is a very small sample size. Following is the cointegration analysis of the variables. Using the Johansen FIML approach for testing the cointegration, there are two basic tests results. The max-eigenvalue and the trace test as explained earlier in this paper. The results of this test are presented below using the given hypothesis decision rule:

H0: R=0 H1: R>0â†’R>0

H0: 0Ë‚Râ‰¤1 H1: R>1

H0: 0Ë‚Râ‰¤2 H1: R>2â†’R>2. where R represents rank and is less than 3.

Co-integration test – Johansen FIML for 20 observations Table 3: Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.740448

40.38981

29.79707

0.0021

At most 1 *

0.544995

17.46023

15.49471

0.0250

At most 2 *

0.213077

4.073633

3.841466

0.0435

Trace test results shows that there exist 3 co-integrating equations at the 5% level

Table 4: Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.740448

22.92958

21.13162

0.0276

At most 1 *

0.544995

13.38660

14.26460

0.0685

At most 2 *

0.213077

4.073633

3.841466

0.0435

Max-eigenvalue test results shows that there exist 1 co-integrating equation at the 5% level.

Co-integration test – Johansen FIML for 60 observations Table 5: Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.192783

24.73114

29.79707

0.1713

At most 1 *

0.123292

12.52387

15.49471

0.1335

At most 2 *

0.084363

5.023705

3.841466

0.0250

Trace test results shows that there exist no co-integrating equations at the 5% level

Table 6: Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.192783

12.20727

21.13162

0.5273

At most 1 *

0.123292

7.500164

14.26460

0.4318

At most 2 *

0.084363

5.023705

3.841466

0.0250

Max-eigenvalue test results shows that there exist no co-integrating equation at the 5% level.

Co-integration test – Johansen FIML for 100 observations Table 7: Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.188373

27.20639

29.79707

0.0967

At most 1 *

0.069223

6.961074

15.49471

0.5822

At most 2 *

2.85E-05

0.002769

3.841466

0.9555

Trace test results shows that there exist no co-integrating equations at the 5% level

Table 8: Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)

Eigenvalue

Trace Statistic

5%

Critical Value

Prob.**

None *

0.188373

20.24532

21.13162

0.0662

At most 1 *

0.069223

6.958305

14.26460

0.4941

At most 2 *

2.85E-05

0.002769

3.841466

0.9555

Max-eigenvalue test results shows that there exist no

## Transformation of poultry farming in India

“The transformation of poultry farming in India from an age-old backyard venture into an organised industrial proposition is the impact of up-to-date technology and sound policies adopted by Governmental and semi-governmental (including private) organisations of the country.”

Poultry – A “Golden Egg” Industry -Future Prospectus Poultry occupies a special place in India, as the eggs and chicken provide important and rich sources of protein, vitamins and minerals. It provides rich organic manure and is an important source of income and employment to millions of farmers and others engaged in allied activities in the poultry industry.

Poultry Industry and India India is the fifth largest producer of eggs and ninth largest producer of poultry meat in the world, producing over 34 billion eggs and about 600,000 tons of poultry meat in 2004. In the overall market for poultry products, India is positioned 17 in World Poultry Production. And analysts estimate that the poultry sector in India has been growing at a much faster rate, along with other industries such as BPO and Securities market. Over the past decade the poultry industry in India has contributed approximately US $229million, to the Gross National Product (GNP).

Consumer Demand and Preferences Poultry always has an increasing demand all over the world. In spite of propaganda raised by organizations promoting vegetarianism and in spite of threat of salmonella, world poultry industry is expanding, as the population is increasing. Per capita consumption is also increasing. Not only that, many countries which are not traditionally poultry – growers are giving incentives to their poultry industry. Indian Government is also giving incentives to this industry by giving incentives to small poultry farmers as well as poultry industry in organized sector as poultry industry generates employment and also provides proteins to masses.

Overall, analysts studies that the total egg consumption is estimated to increase from 34 billion in 2000 and to 106 billion in 2020, while poultry meat consumption is predicted to increase from 687 million kilograms to 1,674 million kilograms. It has been found that egg consumption has grown at a much faster pace, than the consumption of poultry meat. With the continual rise in income, it is estimated to nearly triple by 2020.

Work carried in a poultry industry Every poultry industry carries a common set of work from hatching to culling .Each set of work can be done as a separate business and can support other vendors who are in these industries .Sometimes all set of work can be taken by a same vendor with different business units. In either case the business has a huge set of demand on every part of work.

The various work carried out in a poultry industry can be given as follows

Hatcheries

Poultry Farm and maintenance

Egg production and sales

Poultry Feed and medicine

Culling of spent hens

Hatcheries: A hatchery is a facility where eggs are hatched under artificial conditions,. It is used for economic reasons (i.e. to enhance food supplies ).This will help to create maximum number of chicks on a day and minimize egg breakages.

Poultry Farm and maintenance Poultry Farm and maintenance system is a very important segment of poultry industry. It generally deals with how we house the poultries and maintain it. Poultry housing is a process followed for this purpose by poultry keepers. The housing type adopted depends to a large extent on the amount of ground and the capital available.

Depending upon the available space and cost, farmers use four types of housing..They are

a) Free -range or extensive system

b) Semi- intensive system

c) Folding unit system

d) Intensive system

In India, farmers generally use the free -range extensive system and semi intensive system. For mass production, it is advisable to go for intensive system.

Egg production and sales The egg production period for a hen starts from eighteenth week and it ends at seventy second week. During this period, the egg production will generally follow a normal distribution pattern. The production will be peak from thirty second to fiftieth week.

Namakkal is the highest producer eggs in India. It is called Egg Hub of India. The average volume of the shipment from Namakkal, the country’s high density poultry zone, has been reported to be in the order of 20 million eggs per month.

Egg sales feed both domestic market and international market. In international market the size of the egg should not be big. Generally old bird gives big size eggs. The distribution part of egg may be carried by a third party vendor or it may go through the same persons from whom the feed is bought.

Feed and medicine Poultry feeding is one of the important aspects of poultry science. Poultry feeds are of three types

Starting poultry feed: An all mash ration to be fed to chicks up to the age of 8 weeks.

Growing poultry feed: A ration to be fed to growing chickens after 8 to 20 weeks or until laying commences.

Laying poultry feed: A ration to be fed to laying birds after 20 weeks onwards or after laying commences.

Although we maintain a good sanitary condition for the poultry birds, it is necessary to take preventive action to save it from diseases. Some of the common diseases that affects poultry birds are given below

Fowl Coccidois : This disease is caused by a protozoan parasite of the intestine and can cause very heavy losses in poultry particularly up to the age of 12 weeks.

Ranikhet Disease: A highly infectious and fatal viral disease, it attacks poultry of all ages. Also known as New Castle disease.

Fowl Pox: A viral disease that can affect birds at any age resulting in high mortality rates.

Fowl Coryza :A bacterial disease contaminated through feed, water and by contact through carriers.

Culling of spent hens After seventy two weeks of age, there will be drastic drop in egg production. These old hens are called as spent hens. Here the cost of maintenance is more than the cost of production.The meat of this poultry bird is tough.Hence these birds are sent as a substitute meat to beef. In India, kerala has a huge market for these type of meat.

Substitutes Eggs are used to bind a dish and, when whipped, may also incorporate air making a cake or pudding very light. Vegan egg replacement powders are available from health food shops. This can be useful, especially for tricky foods like meringues.

Preference of White Meat over dark Meat Dark Meat also called as Red meat generally refers to beef, mutton, pork and chevron. These meats are high in cholesterol .

White meat is generally fish and chicken,. They have less content of cholesterol. It is cheap in price compared to dark meat. In today’s environment, taste and preference of customer plays a crucial role in setting the demand of the product..This taste and preference greatly depends on health consciousness factor and price .

As the white meat has a good nutritional value and is less in cholesterol, it is greatly recommended by doctors , physicians and other health inspectors. Also the price is comparatively low with dark meat. These factors generally make a higher performance for white meat over dark meat.

Poultry market In India In India,Namakkal is the major contributor of Indian market. This major poultry producer of India, has been witnessing a positive change in the recent years. The district alone accounts for about 75 per cent of the birds produced in the TamilNadu zone of the National Egg Coordination Committee. The poultry industry in India is vibrant despite a plethora of problems facing it. The zone produces about 2.5 crore eggs a day, with Namakkal contributing 1.75 crore of the total production. The labour-intensive sector provides direct employment to over one lakh people.

The two issues confronting the sector are the ban imposed by some West Asian countries on the import of table eggs from India and the spiralling cost of poultry feed. The volume of export dropped to 20 lakh eggs a day from 50 lakh after some Gulf nations banned egg import in the wake of the outbreak of bird flu in some parts of India. The poultry farms in Namakkal zone have adopted bio-security measures and carry out regular vaccinations. There is a proposal to establish a disease research laboratory for the poultry industry. This would boost the table eggs export from this part of the State.

Export prospects According to Dr P. Selvaraj, Chairman of the National Egg Coordination Committee’s Namakkal Zone, that the buoyancy of the export trade and the market potential opening out for the Indian table eggs in the West Asian market has thrown greater opportunity for the layer egg producers in the region.

World Survey on poultry In a magazine, World-Poultry, an international magazine of poultry business ,a survey was conducted to study the prices of eggs and prices of poultry feed in 45 countries. The prices were converted into U.S. dollars. Conversion ratio that is an index of conversion of feed into eggs was given due weightage. In was certainly an in-depth study of poultry business in the world. The report points out that eggs are cheapest in India, even though feed costs are not cheapest.

Finance for poultry For the self sufficient poultry industry with complete sophistication in the fields of production, breeding stocks, high quality feeds, pharmaceuticals, medicines, poultry vaccines and equipment, the National Agricultural Bank and Rural Development (NABARD) along with commercial and cooperative banks are financing a large number of poultry schemes all over the country for increasing production of eggs and broiler meat. The NABARD refinance for poultry was about Rs.8.08 crores in 1983-84. This increased to Rs. 26 crores in 1985-86. This increased to Rs. 26 crores in 1985-86. This indicates the role that is being plated by banking institutions in poultry development. The NABARD provides refinance assistance for poultry development for the following purposes.

Schemes for poultry breeding including financing of pure line poultry projects to produce grand parent stocks.

Financial assistance to hatcheries to produce commercial one day old broiler or layer chicks from poultry breeding stocks.

Financing for the setting up of commercial egg production farms of different sizes by small, medium and large farmers.

Financing for the setting up of commercial egg production farms of different sizes by small, medium and large commercial broiler farmers.

Financial assistance for the manufacture of poultry medicine and vaccines.

Financial assistance for egg marketing, broiler processing, preservation and marketing of poultry meat.

The production of eggs has now reached an average of 300 eggs per year per bird. The broiler growth now reached the stage of 1.75 kg per bird within 6 to 7 weeks. It is expected that the average growth in layers industry may be around 7 to 8%, while in the case of broilers industry, it may be between 20-25% annum in the next decade.

The transformation of poultry farming in India from an age-old backyard venture into an organised industrial proposition is the impact of up-to-date technology and sound policies adopted by Governmental and semi-governmental (including private) organisations of the country.

Feed Cost all over the world Major cost as production of eggs is for the feed. The feed cost for producing one kilo of eggs is lowest in Zimbabwe , It is 18 cents. Feed costs are highest in Philippines , i.e 45 cents. In India, the figure is 44 cents. In USA it is 15-50 cents The cost in other countries are as follows Pakistan 25 cents, Sri Lanka 24 cents, France 25 cents, and great Britain 24 cents.

Advantages in India These feed cost can be reduced. India is gifted with natural sunshine, cultivable land, and sufficient rains. And there is millions of unemployed .Even by providing water to thirsty lands , we can make poultry feed that can be sufficient for the world poultry. Even by controlling post harvest losses of food grains we will be able to feed world poultry and world diary.

Many countries have to import feed for their poultry industry. India is lucky. We export 24 million tonnes of soya bean cake to Europe, which is the main ingredient of poultry feed. We can export eggs or chicken meat instead of soya bean-cake.

Indian Overview and Conclusion Indian poultry industry is on cross road – considerable progress is already made but we have to go to miles. Most of the global brands are available in India , still there are some opportunities for technology transfer particularly organic brand . And processing is as good as non-existent .There is a great potential which is yet to be tapped.

The market research report ” Vision for Indian Poultry Industry: Current Scenario and Future Prospects” predicts a relatively strong growth for the egg and poultry meat industry, in both the urban and rural areas, in the next two decades.

Future of poultry industry depends on Government and Indian Entrepreneurship. There is lot of scope and future prospects for poultry industry in India. If the market of this industry is rightly tapped by the entrepreneurs, it is a “Golden Egg” industry for the future.

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