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Journal of Economics and Sustainable Development                                                www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011


Supply Response of Rice in Ghana: A Co-integration Analysis
                               John K.M. Kuwornu (Corresponding author)
                Department of Agricultural Economics and Agribusiness, P. O. Box LG 68,
                                University of Ghana, Legon-Accra, Ghana
               Tel: +233 245 131 807 E-mail: jkuwornu@ug.edu.gh / jkuwornu@gmail.com
                                         Maanikuu P. M. Izideen
                Department of Agricultural Economics and Agribusiness, P. O. Box LG 68,
                                University of Ghana, Legon-Accra, Ghana
                          Tel: +233 241 913 985 E-mail: maanikuu@yahoo.com
                                           Yaw B. Osei-Asare
                Department of Agricultural Economics and Agribusiness, P. O. Box LG 68,
                                University of Ghana, Legon-Accra, Ghana
                             Tel: +233 209 543 766 E-mail: yosei@ug.edu.gh

Received: October 1st, 2011
Accepted: October 11th, 2011
Published: October 30th, 2011


Abstract
This study presents an analysis of the responsiveness of rice production in Ghana over the period
1970-2008. Annual time series data of aggregate output, total land area cultivated, yield, real prices of rice
and maize, and rainfall were used for the analysis. The Augmented-Dickey Fuller test was used to test the
stationarity of the individual series, and Johansen maximum likelihood criterion was used to estimate the
short-run and long-run elasticities. The land area cultivated of rice was significantly dependent on output,
rainfall, real price of maize and real price of rice. The elasticity of lagged output (12.8) in the short run was
significant at 1%, but the long run elasticity (4.6) was not significant. Rainfall had an elasticity of 0.004 and
significant at 10%. Real price of maize had negative coefficient of -0.011 and significant at 10%
significance level. This is consistent with theory since a rise in maize price will pull resources away from
rice production into maize production. The real price of rice had an elasticity of 2.01 and significant at 5%
in the short run and an elasticity of 3.11 in the long run. The error correction term had the expected negative
coefficient of -0.434 which is significant at 1%. It was found that in the long run only real prices of maize
and rice were significant with elasticities of -0.46 and 3.11 respectively. The empirical results also revealed
that the aggregate output of rice in the short run was found to be dependent on the acreage cultivated, the
real prices of rice, rainfall and previous output with elasticities of 0.018, 0.01, 0.003 and 0.52 respectively.
Real price of rice and area cultivated are significant 10% level of significance while rainfall and lagged
output are significant 5%. In the long run aggregate output was found to be dependent on acreage cultivated,
real price of rice, and real price maize with elasticities of 0.218, 0.242 and -0.01 respectively at the 1%
significance level. The analysis showed that short-run responses in rice production are lower than long-run
response as indicated by the higher long-run elasticities. These results have Agricultural policy implications
for Ghana.

Key Words: Supply response, Rice, Error Correction Model, Co-integration Analysis, Ghana

1. Introduction
One of the most influential policy prescriptions for low-income countries ever given by development
economists has been to foster industrialization by withdrawing resources from agriculture (e.g., Lewis,
1954). There is robust evidence that the majority of policy makers followed this prescription at least until
the mid-1980s. The results of a comprehensive World Bank study (Krueger et al., 1992), for example, show
for the period 1960 –1985 that in most countries examined, agriculture was taxed both directly via

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Journal of Economics and Sustainable Development                                                  www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

interventions in agricultural markets and indirectly via overvalued exchange rates and import substitution
policies. It is not obvious whether the disincentives for agricultural production have continued to exist since
the mid-1980s. On the one hand, most developing countries have adopted structural adjustment programs
which explicitly aim at a removal of the direct and indirect discrimination against agriculture. But, on the
other hand, it is known that many of these programs were not fully implemented, especially in Sub-Saharan
Africa (Kherallah et al., 2000; Thiele and Wiebelt 2000; World Bank, 1997). Hence it can be concluded that
a certain degree of discrimination still prevails.
One of the most important issues in agricultural development economics is supply response of crops. This
is because the responsiveness of farmers to economic incentives determines agricultures’ contribution to the
economy especially where the sector is the largest employer of the labour force. This is often the case in
third world low income countries. Agricultural pricing policy plays a key role in increasing farm production.
Supply response is fundamental to an understanding of this price mechanism (Nerlove and Bachman,
1960).
There is a notion that farmers in less developed countries respond slowly to economic incentives such as
price and income. Reasons cited for poor response vary from factors such as constraints on irrigation and
infrastructure to a lack of complementary agricultural policies. The importance of non-price factors drew
adequate attention in the literature: rainfall, irrigation, market access for both inputs and output, and literacy.
The reason cited for a low response to prices in less developed economies is the limited access to input and
product markets or high transaction costs associated with their use. Limited market access may be either
due to physical constraints such as absence of proper road links or the distances involved between the roads
and the markets, or institutional constraints like the presence of intermediaries (Mythili, 2008).
In Ghana, the goals of agricultural price policy are among others fair incomes for farmers, low food prices
for urban consumers, cheap raw materials for manufacturing (Ministry of Food and Agriculture (MOFA),
2005). Price support was one policy that was used by government in targeting these goals. Price support has
been used in many countries across the world. The prices of major commodities have been set below world
prices using subsidies and trade barriers, guaranteed prices (act as floors) and domestic market forces
determining actual prices (Food and Agriculture Organization (FAO), 1996). The effect of liberalization on
the growth of agriculture crucially depends on how the farmers respond to various price incentives.
For many low-income countries, the impact of structural reforms on economic growth and poverty
alleviation crucially depends on the response of aggregate agricultural supply to changing incentives.
Despite its policy relevance, the size of this parameter is still largely unknown (Thiele, 2000).
Rice is a very important crop especially for those areas where it is produced. Rice is gradually taking the
position as the main staple food for the majority of families in Ghana especially in the urban centres. The
growth in consumption of rice has been not been matched with a corresponding growth in local production
of the crop. Per capita consumption of rice has steadily increased over the years since the 1980s from about
12.4kg/person /year in 1984 to about 20kg/person /year (Statistical Research and Information Division
(SRID), 2005). It continued to rise to 38kg/person/year in 2008 (National Rice Development Policy
(NRDP), 2009). Over the last decade rice per capita consumption has increased by more than 35%. This is
attributed mainly to the rapid urbanization and changes in food consumption patterns. It is estimated based
on population and demand growth rates that per capita consumption will reach 41.1kg by 2010 and 63.0kg
by 2015 giving an aggregate demand of 1,680,000tons/year (Statistical Research and Information Division
(SRID), 2005). Changes in population dynamics and the taste or preference for foreign products is
contributing to this trend. These changes are significant considering the rate of population growth. Ghana’s
population has grown by about 70% from 12.3 million in 1984 to about 21 million in 2004. The population
is now 24 million (Ghana Statistical Service (GSS), 2011).
The growing quantum of rice imports into the country has also been triggered partly by the fall in world
market price. This is largely due to increased output levels in India and South Asia. Massive subsidies in
rice exporting countries have also contributed to this phenomenon. Despite all these developments there
seem to be a lack of coherent and comprehensive national policy for rice in Ghana. From the period of
economic recovery and structural adjustment, the country has had to embark on trade liberalization and the
removal of all forms of subsidies on agriculture. These policies no doubt have adversely affected rice
production in the country. The smallholder rural farmer is faced with unfair competition from abroad. The
                                                        2
Journal of Economics and Sustainable Development                                                   www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

role of incentives to farmers has generally been sidelined or ignored in most developing countries due to
conditions of trade liberalization and global trade integration. However, a number of empirical studies in
other developing economies addressed the question of farmers’ response to economic incentives and
efficient allocation of resources (e.g. Chinyere, 2009). The agricultural sector in Ghana has undergone
various policy regimes which has affected both the factor and product market resulting in changes in the
structure of market incentives (prices) faced by farmers. Most of these policies have, however, not been
crop specific and therefore has wide variations in the quantum of changes in the incentives. This study
therefore follows the supply response framework of analysis to examine the dynamics of the supply of rice
in Ghana. Effort in this direction will have to be preceded by a thorough analysis of the factors that affects
the supply of rice. These teething problems lead to the following research questions;
How responsive is rice production to price and non-price factors? What are the long run and short run
elasticities of rice production? What are the trends in area cultivated, output and real prices of output?
Therefore, objective of the study is threefold: (a) Examine the acreage and output response of rice
production in Ghana. (b) Estimate and compare the long run and short run elasticities of rice production.
(c) Analyze the trends in output, area cultivated and real prices.
The rest of the paper is structured as follows. Section 2 outlines the methodology. In section 3 we present
the empirical applications and the results. Section 4 provides the conclusions.

2.0     Methodology
This section presents the methodology of the study.
2.1 Linear Regression
Linear regression was conducted to determine the growth rates of the variables over the study period. Time
in years was regressed on acreage cultivated, aggregate output, real price of rice separately.
2.2 Time series analysis
In empirical analysis using time series data it is important that the presence or absence of unit root is
established. This is because contemporary econometrics has indicated that regression analysis using time
series data with unit root produce spurious or invalid regression results (e.g. Townsend, 2001). Most time
series are trended over time and regressions between trended series may produce significant parameters
with high R2s, but may be spurious or meaningless (Granger and Newbold, 1974). When using the classical
statistical inference to analyze time series data, the results are only stationary when the series are stationary.
The solution to this problem was initially provided by Box and Jenkins (1976), by formulating regressions
in which the variables were expressed in first difference. Their approach simply assumed that
non-stationary data can be made stationary by repeated differencing until stationarity is achieved and then
to perform the regression using these differenced variables. However, according to Davidson et al., (1978),
this process of repeated differencing even though leads to stationarity of the series; it is achieved at the
expense of losing valuable long run information. This posed a new challenge to time series econometrics.
The concept of cointegration was introduced to solve these problems (Granger, 1981; Engle and Granger,
1987). By using the method of cointegration an equation can be specified in which all terms are stationary
and so allow the use of classical statistical inference. It also retains information about the long run
relationship between the levels of variables, which is captured in the stationary co-integrating vector. This
vector will comprise the parameters of the long run equilibrium and corresponds to the parameters of the
error correction term in the second stage regression (Mohammed, 2005). The cointegration approach takes
into consideration the long-run information such that spurious results are avoided.

2.3 Stationarity Tests

A data series is said to be stationary if it has a constant mean and variance. That is the series fluctuates around
its mean value within a finite range and does not show any distinct trend over time. In a stationary series
displacement over time does not alter the characteristics of a series in the sense that the probability
distribution remains constant over time. A stationary series is thus a series in which the mean, variance and
covariance remain constant over time or in other words do not change or fluctuate over time. In a stationary

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Journal of Economics and Sustainable Development                                                www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

series the mean always has the tendency to return to its mean value and to fluctuate around it in a more or less
constant range, while a non-stationary series has a changing mean at different points in time and its variance
change with the sample size (Mohammed, 2005). The conditions of stationarity can be illustrated by the
following:

Yt = ѳYt-1 + µ t                                     t=1………T                                               (1)
Where µ t is a random walk with mean zero and constant variance. If ѳ < 1, the series Yt stationary and if
ѳ = 1 then the series Yt is non-stationary and is known as random walk. In other words the mean, variance
and covariance of the series Yt changes with time or have an infinite range. However Yt can be made
stationary by differencing. Differencing can be done multiple times on a series depending on the number of
unit roots a series has. If a series becomes stationary after differencing d times, then the series contains d
unit roots and hence integrated of order d denoted as I (d). Thus, in equation (1) where ѳ = 1, Yt has a unit
root. A stationary series could also exhibit other properties such as when there are different kinds of time
trends in the variable.
The DF (Dickey-Fuller)-statistic used in testing for unit root is based on the assumption that µ t is white
noise. If this assumption does not hold, it leads to autocorrelation in the residuals of the OLS regressions
and this can make invalid the use of the DF-statistic for testing unit root. There are two approaches to solve
this problem (Towsend, 2001). In the first instance the equations to be tested can be generalized. Secondly
the DF-statistics can be adjusted. The most commonly used is the first approach which is the Augmented
Dickey-Fuller (ADF) test. µ t is made white noise by adding lagged values of the dependent variable to the
equations being tested, thus:
∆Yt = (ѳ1 – 1) Yt-1 +           IYt-I + µ t                                                                 (2)
∆Yt = α2 + (ѳ2 – 1) Yt-1 +            I ∆Yt-I + µ t                                                         (3)
∆Yt= α3+β3t(ѳ3–1)Yt-1+            I ∆Yt-I + µ t                                                            (4)
The ADF test uses the same critical values with DF. The results of the ADF test for unit roots for each of
the data series used in this study are presented in in the next section using equation (4) where Yt is the
series under investigation, t is the time trend, α3 is the constant term and µ t are white noise residuals.
Eviews was used in the analysis, all the data series was tested for stationarity and the results are presented
in section three.
2.4 Cointegration
Cointegration is founded on the principle of identifying equilibrium or long run relationships between
variables. If two data series have a long run equilibrium relationship it implies their divergence from the
equilibrium are bounded, that is they move together and are cointegrated. Generally for two or more series
to be co-integrated two conditions have to be met. One is that the series must all be integrated to the same
order and secondly a linear combination of the variables exist which is integrated to an order lower than
that of the individual series. If in a regression equation the variables become stationary after first
differencing, that is I (1), then the error term from the cointegration regression is stationary,         I (0)
(Hansen and Juselius, 1995). If the cointegration regression is presented as:
Yt = α + βXt + µ t                                                                                         (5)
where Yt and Xt are both I (1) and the error term is I (0), then the series are co-integrated of order I (1,0 )
and β measures the equilibrium relationship between the series Yt and Xt and µ t is the deviation from the
long-run equilibrium path. An equilibrium relationship between the variables implies that even though Yt
and Xt series may have trends, or cyclical seasonal variations, the movement in one are matched by
movements in the other. The concept of cointegration has implications for economists. The economic
interpretation that is accepted is that if in the long-run two or more series Yt and Xt themselves are
non-stationary, they will move together closely over time and the difference between them is constant
(stationary) (Mohammed 2005).
2.4.1 Testing for Cointegration
There are two most commonly used methods for testing cointegration. The Augmented Dickey-Fuller
residual based test by Engle and Granger (1987), and the Johansen Full Information Maximum Likelihood
(FIML) test (Johansen and Juselius, 1990). For the purpose of this study the Johansen Full Information
Maximum Likelihood test is used due to its advantages. The major disadvantage of the residual based test is
                                                       4
Journal of Economics and Sustainable Development                                                   www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

that it assumes a single co-integrating vector. But if the regression has more than one co-integrating vector
this method becomes inappropriate (Johansen and Juselius, 1990). The Johansen method allows for all
possible co-integrating relationships and allows the number of co-integrating vectors to be determined
empirically.
2.4.2 Johansen Full Information Maximum Likelihood Approach
The Johansen approach is based on the following Vector Autoregression
Zt = AtZt-1 + … + AkZt-k + µ t                                                                           (6)
Where Zt is an (n×1) vector of I(1) variables (containing both endogenous and exogenous variables), At is
(n×n) matrix of parameters and µ t is (n×1) vector of white noise errors. Zt is assumed to be nonstationary
hence equation (6) can be rewritten in first difference or error correction form as;
∆Zt = Γ1∆Zt-1 + … + Γk-1∆Zt-k+1 + πZt-k + µ t                                                             (7)

where Γ1 = - ( 1- A1 - A2 - … -Ai), (i = 1, …, k-1) and π = - (1- A1-A2- …-Ak).
Γ1 gives the short run estimates while π gives the long run estimates. Information on the number of
co-integrating relationships among variables in Zt is given by the rank of the matrix π. If the rank of π
matrix r, is 0 < r > n, there are r linear combinations of the variables in Zt that are stationary. Thus π can be
decomposed into two matrices α and β where α is the error correction term and measures the speed of
adjustment in ∆Zt and β contains r co-integrating vectors, that is the cointegration relationship between
non-stationary variables. If there are variables which are I(0) and are significant in the long run
co-integrating space but affect the short run model then equation (7) can be rewritten as:
∆Zt = Γ1∆Zt-1 + πZt-k + vDt + µ t                                                                               (8)
where Dt represents the I(0) variables.
To test for co-integrating vector two likelihood ratio (LR) tests are used. The first is the trace test statistic;

Λtrace = -2lnQ = -T                     i)                                                                   (9)
Which test the null hypothesis of r co-integrating vectors against the alternative that it is greater than r. The
second test is known as the maximal-eigen value test:
Λmax = -2 ln(Q: r 1 r + 1 = -T ln(1-λr+1 )                                                                   (10)
which test the null hypothesis of r co-integrating vectors against the alternative of r+1 co-integrating
vectors. The trace test shows more robustness to both skewness and excess kurtosis in the residuals than the
maximal eigen value test (Harris, 1995). The error correction formulation in (7) includes both the difference
and level of the series hence there is no loss of long run relationship between variables which is a
characteristic feature of error correction modeling.
It should be noted that in using this method, the endogenous variables included in the Vector
Autoregression (VAR) are all I(1), also the additional exogenous variables which explain the short run
effect are I(0). The choice of lag length is also important and the Akaike Information Criterion (AIC), the
Scharz Bayesian Criterion (SBC) and the Hannan-Quin Information Criterion (HQ) are used for the
selection.
According to Hall (1991) since the process might be sensitive to lag length, different lag orders should be
used starting from an arbitrary high order. The correct order is where a restriction on the lag length is
rejected and the results are consistent with theory.
2.5 Error Correction Models (ECMs)
The idea behind the mechanism of error correction is that a proportion of disequilibrium from one period is
corrected in the next period in an economic system (Engle and Granger, 1987). The process of making a
data series stationary is either done by differencing or inclusion of a trend. A series that is made stationary
by including a trend is trend stationary and a series that is made stationary by differencing is difference
stationary. The process of transforming a data series into stationary series leads to loss of valuable long run
information (Engle and Granger, 1987). Error correction models helps to solve this problem.
The Granger representation theorem is the basis for the error correction model which indicates that if the
variables are cointegrated, there is a long-run relationship between them and can be described by the error
correction model. The following equation shows an ECM of agricultural supply response involving the
variables Y and X in its simplest form:
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Journal of Economics and Sustainable Development                                                       www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

∆Yt = α∆Xt – ѳ(Yt-1 – γXt-1)+µ t                                                                            (11)
Where µ t is the disturbance term with zero mean, constant variance and zero covariance. Parameter α takes
into account the short run effect on Y of the changes in X, while γ measures the long-run equilibrium
relationship between Y and X that is:
Yt = γXt + µ t                                                                                              (12)
Where Yt-1 – γYt-1 + µ t-1 measures the divergence (errors) from long-run equilibrium. Also ѳ measures the
extent of error correction by adjustment in Y and its negative sign indicates that the adjustment is in the
direction which restores the long-run relationship (Hallam and Zanoli, 1993). In order to estimate equation
(5), Engle and Granger (1987) proposed a two stage process. Firstly the static long run cointegration
regression (6) is estimated to test cointegration between the two variables. If cointegration exists the lagged
residuals from equation (5) are used as error correction term in the Error Correction Model (in equation 6)
to estimate the short run equilibrium relationship between the variables in the second stage. The validity of
the Error Correction Models (ECMs) depends upon the existence of a long-run or equilibrium relationship
among the variables (Mohammed, 2005).
The Error Correction Model (ECM) has several advantages. It contains a well-behaved error term and
avoids the problem of autocorrelation. It allows consistent estimation of the parameters by incorporating
both short-run and long-run effects. Most importantly all terms in the ECM are stationary. It ensures that no
information on the levels of the variables is lost or ignored by the inclusion of the disequilibrium terms
(Mohammed, 2005). ECM solves the problems of spurious correlation because ECMs are formulated in
terms of first difference which eliminates trends from the variables (Ganger and Newbold, 1974). It avoids
the unrealistic assumption of fixed supply based on stationary expectations in the partial adjustment model.
2.6 Supply Response Models
This study estimated the total area cultivated and aggregate output of rice in Ghana using double
logarithmic regression models.
Area cultivated of rice (lgarea) is a function of own price (Lrp), rainfall (lgrain), aggregate output (lgoutput)
and price of maize (lmp). The equation used for the regression is:
Lgarea = f1 (Lgoutput, Lrp, Lgrain, Lmp)                                                                    (13)
Aggregate output of rice (lgoutput) is a function of the area cultivated (lgarea), the price of rice (Lrp), price
of maize (Lmp), rainfall (lgrain). The estimating equation is:
Lgoutput = f2 (lgrain, Lrp, Lmp, Lgarea)1.                                                                   (14)

3. Empirical Application and Results
3.1 Results for linear regression
Table 1: The results of the regression analysis for the trend of the variables against time
Variable      Coefficient      Std error   t-statistic             R2           F-statistic                    Prob
Lgarea        1.334274         1.149167    1.61079            0.035154          0.253046                       0.2530
Lgoutput      -13.86625        68.09380    -0.20364           0.005889          0.844432                       0.8444
lgyield       -25.99348        35.51112    -0.73198           0.071100          0.487957                       0.4880
Lrp           2.588941         5.221601    4.956136           0.399186          24.58311                       0.0000

As can be seen from table 1 the results indicated that the regression for area, output and yield turned out
insignificant. Only the trend of real price of rice was significant at 1% significance level. Real rice price
yielded a positive coefficient of 2.589 which implies that for each year the real price of rice grew by 2.589
units.
3.2 Unit Root Test Results
As a requirement for cointegration analysis the data was tested for series stationarity and to determine the
order of integration of the individual variables. For cointegration analysis to be valid all series must be
integrated of the same order usually of order one (Towsend, 2001). Eviews was used to perform these tests.

1
  Price of maize is included because we assume that the same resources (land type, fertilizer etc.) can be used to
produce both maize and rice. Hence, a rise in the price of maize will pull resources away from rice production to maize
production.
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Journal of Economics and Sustainable Development                                               www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

The data series on annual acreage cultivated (Lgarea), aggregate output (Lgoutput), aggregate yield
(Lgyield), real price of rice (Lrp), real price of maize (Lmp), rainfall (Lgrain) was tested for unit root for
the study period 1970 - 2008. The Augmented Dickey-Fuller test was used for this test. The results are
presented below.


Table 2: Results of unit root test at levels
Series            ADF test             Mackinnon              Lag-length         Prob           Conclusion
                  statistic            critical value
Lgarea            2.221125             3.615588                       2          0.2024         Non-stationary
Lgoutput            1.519278             5.119808                     2          0.4582         Non-stationary
Lgyield             0.103318             4.580648                     2          0.9419         Non-stationary
Lmp                 0.378290             3.621023                     2          0.9026         Non-stationary
Lrp                 1.429037             3.615588                     2          0.5579         Non-stationary

Lgrain              3.006033             2.963972                     7          0.0457         Stationary


The results of the unit root test after first differencing are presented in table 3.

Table 3: Results of unit root test at first differences
Series            ADF test            Mackinnon                Lag-length        Prob          Conclusion
                  statistic           critical value
Lgarea            7.517047            3.621023                       2           0.0000        I(1)
Lgoutput          9.577098            3.621023                       2           0.0000        I(1)
Lgyield           7.517047            3.621023                       2           0.0000        I(1)
Lmp               10.02189            3.621023                       2           0.0000        I(1)
Lrp               6.346392            3.626784                       2           0.0000        I(1)

Note; All variables are in log form. The ADF method test the hypothesis that H0 : X ~ I(1), that is, has unit
root (non-stationary) against H1 : X ~ I(0), that is, no unit root (stationary). The Mackinnon critical values
for the rejection of the null hypothesis of unit root are all significant at 1%. Lgarea denotes log of area
cultivated, Lgoutput denotes log total output, Lgyield denotes log of yield, Lmp denotes log of maize price,
Lrp denotes log of rice price and Lgrain denotes log of rainfall.
The results of the unit root tests showed that all the series are non-stationary at levels except for rainfall
which is stationary at levels as shown in table 2 above. However as expected all the non-stationary series
became stationary after first differencing. From table 2 the null hypothesis of unit root could not be rejected
at levels since none except rainfall of the ADF test statistics was greater than the relevant Mackinnon
Critical values. Hence the null of the presence of unit root is accepted.
However the hypothesis of unit root in all series was rejected at 1% level of significance for all series after
first difference since the ADF test statistics are greater than the respective Mackinnon critical values as
shown in table 3 above.
3.3 Cointegration Results
When the order of integration of the data series have been established, the next step in the process of
analysis is to determine the existence or otherwise of cointegration in the series. This is to establish the
existence of valid long-run relationships between variables. Basically there are two most commonly used
methods to test for cointegration. These were suggested by Engle and Granger (1987) and Johansen and
Juselius (1990). This study applies the Johansen approach which provides likelihood ratio tests for the
presence of number of co-integrating vectors among the series and produces long-run elasticities. The Error
correction model was then used to estimate short-run elasticities.
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Journal of Economics and Sustainable Development                                              www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.6, 2011

3.3.1 Supply Response of Rice Output
Firstly, the Vector Error Correction Modeling (VECM) procedure using the Johansen method involves
defining an unrestricted Vector Autoregression (VAR) using the following equation.
Zt=AtZt-1+…+AkZt-1+µ t                                                                            (15)
Likelihood Ratio (LR) tests were conducted with maximum of three lags due to the short time series. The
results are shown in table 4.




Table 4: Results for lag selection output model
 VAR Lag Order Selection Criteria
 Endogenous variables: OUTPUT AREA MP RP
 Exogenous variables: C RAIN

 Lag        LogL             LR                FPE               AIC               SC               HQ

0           -1208.968        NA                 6.64e+32             86.92626      87.30689*         87.04262
1           -1184.928        37.77692           3.84e+32             86.35198      87.49387          86.70107
2           -1174.563        13.32579           6.37e+32             86.75452      88.65766          87.33633
3           -1138.068         36.49514*         1.92e+32*             85.29058*    87.95499           86.10511*

 * indicates lag order selected by the criterion
 LR: sequential modified LR test statistic (each test at 5% level)
 FPE: Final prediction error
 AIC: Akaike information criterion
 SC: Schwarz information criterion
 HQ: Hannan-Quinn information criterion
 LogL: Log-likelihood


The results indicate that the Akaike Information Criterion (AIC) and the Hannan-Quin information criterion
(HQ) selected lag order three while the Scharz Bayesian Criterion (SBC) selected the lag order zero. Thus,
this study selects the lag order three as the order for the VAR models. The next step in the Johansen method
is to test for the number of co-integrating vectors among the series in the model.
The results for the cointegration test imply that both the trace test and the maximum eigen value test selects
the presence of one co-integrating vector, thus it can be concluded that the variables in the model are
co-integrated. The Johansen model is a form of Error Correction Model. When only one co-integrating
vector is established its parameters can be interpreted as estimates of long run co-integrating relationship
between the variables (Hallam and Zanoli, 1993). This implies that the estimated parameter values from
this equation when normalized on output are the long run elasticities for the model. Eviews automatically
produces the normalized estimates. These coefficients represent estimates of long-run elasticities with
respect to area cultivated, real price of rice and real price of maize. The normalized cointegration equation
for rice output is given below;
Output=0.216748area+0.241522rp-0.009975mp-12841.69                                                       (16)
Since cointegration has been established among the variables, then the dynamic ECM can be used for
supply response analysis since it provides information about the speed of adjustment to long run
equilibrium and avoids the spurious regression problem between the variables (Engle and Granger, 1987).
The ECM for rice output is presented as;
∆output = α0+         1i∆outputt-i +       2i∆areat-i +       3i∆rpt-I +    4i∆mpt-I + 5rain – θECt-I   (17)
Where θECt-I = α (β1outputt-I – β2areat-I – β3rpt-I – β4mpt-I)
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In the ECM model above, the α’s explain the short run effect of changes in the explanatory variables on the
dependent variable whereas β’s represent the long run equilibrium effect. θECt-I is the error correction term
and correspond to the residuals of the long run cointegration relationship, that is the normalized equation
(16). The negative sign on the error correction term indicates that adjustments are made towards restoring
long run equilibrium. This representation ensures that short run adjustments are guided by and consistent
with the long run equilibrium relationship. This method provides estimates for the short run elasticities, that
is the coefficients of the difference terms and, whereas the parameters from the Johansen cointegration
regression are estimates of the long run elasticities (Townsend and Thirtle, 1994). In selecting the best ECM
estimates, models with lag lengths are estimated and those with insignificant parameters are eliminated.
Note that the estimates presented above represent the short run effect of the explanatory variables on the
dependent variable. The long run effect is captured by the estimates of the normalised Johansen regression
results presented in equation (16). The diagnostic tests are the t-ratio test of the coefficients, LM test for
autocorrelation and the Jarque Berra test for normality of residuals.
Table 5: ECM results for rice output
Variable                  Coefficient                  t-statistic             Prob

∆lgarea                   0.017611                   0.668907                   0.0510

∆lgoutput(-1)             0.523393                   2.283959                   0.0319

lgRain                    0.003740                   0.997918                   0.0328

∆lgmp                     -0.001107                  -0.298174                  0.7683

∆lgrp                     0.009922                   0.307758                   0.0761

Residual                  -0.174253                  -1.321005                  0.0043

R2 (0.754242)             F-statistic (2.523411)     Prob(F-stat) 0.058261


It indicates that aggregate rice output is dependent on area cultivated, previous year’s output, and previous
year’s price of rice. The coefficient of maize price was not significant.
An R2 of 0.754 indicates that 75% of the variation in the dependent variable can be accounted for by
variation in the explanatory variables. The results indicate that area cultivated was significant at 10%, with
elasticity of 0.0176, which implies that a one percent increase in area cultivated of rice will lead to a
0.018 % increase in output in the short run. This is rather on the low side. This can be attributed to the fact
that an increase in the land cultivated without a necessary increase in the resources of the farmer will
increase adverse effect on the minimal resources available to the farmer hence resulting in this marginal
increase in output. This result also indicates that the productivity of the farmer reduces with increase in
farm size. The long run elasticity of area cultivated is 0.2167 (in equation (16)) is higher than the 0.0176 for
the short run. This indicates that over the long run farmers adjust their farm sizes more to output than in the
short run. That is, a 1% increase in area cultivated will lead to 0.217% increase in output in the long run.
Lagged output has elasticity of 0.523393 in the short run and is significant at the 5% significance level
implying that a one percent increase in output in a year will lead to a 0.52% increase in output the
subsequent year. This simply means that an increase in output will make capital available for the farmer to
invest in the subsequent year’s rice production activities. This is only true if the additional resource is
invested into the following year’s rice production activities.
Rainfall is significant at 5%. An elasticity of 0.003740 for rainfall indicates that a 1% increase in rainfall
will result in a 0.004% rise in output. This low impact of rainfall can be explained by the fact that a large
proportion of total rice output is produced under the irrigation projects across the country. This makes the
response of output to rainfall highly inelastic.
A coefficient of 0.009922 which is significant at 10% for real price of rice indicates that a 1% rise in real
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prices will lead to 0.01% increase in output the subsequent year in the short run. This makes prices also
inelastic. This low rice price elasticity suggests that farmers do not always necessarily benefit from
increasing prices due to the structure of the marketing system. Middlemen and other marketing channel
members purchase the rice from farmers at the farm gate and thereafter transport it to market centres to be
sold. Hence when there is a rise in prices a little fraction of it is transmitted to the famer. The low price
elasticity could also be attributed to the fact that farmers are hindered by an array of constraints such as
land tenure issues, rainfall variability, lack of capital resources and credit facilities which limits their
capacity to respond to price incentives. The long run coefficient of 0.2415 indicates that 1% percent
increase in real prices will lead to a 0.24% increase in output. The long run elasticity far exceeds the short
run. This is because over the long run when prices show a continual rise, farmers are able to accumulate
capital enough to enhance production.
The residual which is the error correction term is significant at 1% and has the expected negative sign. It
measures the adjustment to equilibrium. Its coefficient of -0.174 indicates that the 17.4% deviation of rice
output from long run equilibrium is corrected for in the current period. This slow adjustment can be
attributed to the fact that famers in the short run are constrained by technical factors as mentioned earlier
which limits their ability to adjust immediately to price incentives.
In the long run maize price is significant at 1%. With a negative coefficient of 0.009975 it implies that a 1%
increase in the price of maize will lead to 0.01% reduction in the output of rice. This is to say that resources
will be diverted to maize production relative to rice production leading to the fall in rice output. However,
Maize price was not significant in the short run. The results of the LM test of serial correlation for up to
fifth order show that there is no serial correlation in the data set.
3.3.2 Supply Response of Area Cultivated
Testing for the selection of lag length yielded the same results for both the acreage and output models.
Hence the same lag order three is therefore used for the Vector Error Correction model (VEC).
The trace test selects one co-integrating vector while the eigen value test selects two co-integrating vectors.
But since the trace test is the more powerful test (Mohammed, 2005), the result from the trace test is used
here. Thus the acreage model has one co-integrating vector. The normalised cointegration equation for area
cultivated is given as;
lgarea =4.613649lgoutput+3.11429lrp–0.46020lmp–59247                                                         (18)
The ECM for area cultivated is presented as;
∆lgarea = α0 +         1i ∆lgoutputt-i +       2i ∆lgareat-i +        3i ∆lgrpt-I +   4i ∆lgmpt-I +   5l lgrain –
θECt-I                                                                                                       (19)
Where θECt-I = α (β1areat-I – β2outputt-I – β3rpt-I – β4mpt-I)

The result of the estimated ECM is given in table 6 below.
Table 6: ECM results for area cultivated
Variable               Coefficient          t-statistic                    Prob
∆lgarea(-1)            0.169747             1.343021                       0.1192
∆lgoutput(-1)          12.80810             1.816372                       0.0824
lgRain                 0.003984             0.138622                       0.0891
∆lmp(-1)               -0.011350            -0.401819                      0.0691
∆lrp(-1)               2.016747             1.440683                       0.0265
Residual               -0.435411            -1.043641                      0.0047
R2 (0.770669)          F-statistic          Prob(F-stat) 0.017301
                       (1.707148)


An R2 of 0.77067 indicates that 77% 0f the variation in the dependent variable is accounted for by variation
in the explanatory variables. Output is significant at 10% with an elasticity of 12.80 indicating that a 1%
increase in output this year will increase land cultivated in the subsequent year by 12.8% in the short run.
Thus acreage cultivated is highly elastic with respect to output. An increase output level gives the farmers
an opportunity to acquire necessary resources and equipment to put more land under cultivation. Output
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elasticity is even higher than that for real prices. This can be attributed to the fact that a large proportion of
output is for household consumption and thus is independent of prices. Increased output alone is enough
motivation to increase acreage under cultivation. In the long run output is insignificant. This because in the
long run land will get exhausted and output will be dependent on productivity (and not the area of land
cultivated).
Rainfall is significant at 10% with a coefficient of 0.003984; this implies a 1% increase in rainfall leads to
0.004% increase in area cultivated. This low response to rain can be attributed to the fact that large areas
under rice cultivation are in irrigated fields which depends less on rainfall for cultivation. Rainfall is an
exogenous variable (I(0)), hence it measures short run effect.
Maize price is significant at 10% with a negative coefficient of -0.011350 in the short run and -0.460 in the
long run. This means over the long run if maize prices are continually increasing farmers will commit more
resources into maize farming relative to rice farming. Thus as the price of maize rises area under rice
cultivation reduces both in the short and long run. In the short run a 1% increase in maize price reduces area
cultivated of rice by 0.0114% and by 0.46% in the long run.
Real price of rice was significant at 5% with elasticities of 2.017 and 3.11 in the short run and long run
respectively, thus price is elastic. This implies a 1% percent increase in real price of rice will result 2.017%
and 3.11% increase in acreage cultivated in the short run and long run respectively. This is expected since
in the long run farmers are able to adjust to overcome some the major challenges of production and hence
are able to adjust more to price incentives.
The error correction term has the expected negative sign and with a coefficient of 0.4354 indicating that
43.54% of the deviation from long run equilibrium is corrected for in the current period.
The results of the test for serial correlation show that is no autocorrelation in the series. The Jarque-Berra
test statistic of 10.18 with probability value of 0.25 implies that the residuals are normally distributed.
4. Conclusions
Economic theory in the past had been based on the assumption that time series data is stationary and hence
standard statistical techniques (Ordinary Least Squares regression (OLS)) designed for stationary series was
used. However, it is now known that many time series data are non-stationary and therefore using
traditional OLS methods will lead to invalid or spurious results. To address this problem, the method of
differencing was introduced. Differencing according to Granger however leads to loss of valuable long-run
information. Granger and Newbold (1974) introduced the technique of cointegration which takes into
account long-run information therefore avoiding spurious results while maintaining long-run information.
This study presents an analysis of the responsiveness of rice production in Ghana over the period
1970-2008. Annual time series data of aggregate output, total land area cultivated, yield, real prices of rice
and maize, and rainfall were used for the analysis.
The Augmented-Dickey Fuller test was used to test the stationarity of the individual series. The Johansen
maximum likelihood criterion is used to estimate the short-run and long-run elasticities.
The trend analysis for rice output, rice price, acreage cultivated, and yield revealed that only rice price is
significant at 1% significance level. The results imply that for each year, the price of rice will increase by
2.589 units.
All the time series data that was used were tested for unit root. They were found to be non-stationary at
levels but stationary after first differencing at the one percent significance level except for rainfall which
was stationary at levels at the 5% significance level. The Likelihood Ratio tests selection of lag order
selected order three by the AIC and HQ criteria for both models. The Johansen cointegration test selected
one cointegration vector (in both rice output and rice price models) indicating that the variables are
co-integrated. The diagnostic tests of serial correlation and normality test was done using the Lagrange
Multiplier test for autocorrelation and the Jarque-Berra test for normality of residuals. The results indicate
no serial correlation and normally distributed residuals.
The land area cultivated of rice was significantly dependent on output, rainfall, real price of maize and real
price of rice. The elasticity of lagged output was 12.8 in the short run and was significant at 1%. However,
this elasticity was not significant in the long run.
Rainfall had an elasticity of 0.004 and significant at 10%. Also real price of maize had negative coefficient
of -0.011 which was significant at 10%. This is consistent with theory since a rise in maize price will pull
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resources away from rice production into maize production. The coefficient of the real price of maize
estimated by Chinere (2009) was -0.066 for rice farming in Nigeria. This is higher than the -0.011 estimated
for Ghana (in this study).
The real price of rice had an elasticity of 2.01 and significant at 5% in the short run and an elasticity of 3.11
in the long run. The error correction term had the expected negative coefficient of -0.434 which is
significant at 1%. It was found that in the long run only real prices of maize and rice were significant with
elasticities of -0.46 and 3.11 respectively. The error correction estimated by Chinere (2009) was -0.575.
This indicates that adjustment to long run equilibrium is faster in Nigeria. This could be attributed to better
agricultural infrastructure in Nigeria compared to Ghana. The error correction for Indian rice in the rice
zone as estimated by Mohammed (2005) was -0.415. This could be attributed to the fact that Indian
agriculture was highly constrained by land problems hence leaving room for little adjustments in terms of
increasing acreage cultivated.
The aggregate output of rice in the short run was found to be dependent on the acreage cultivated, the real
prices of rice, rainfall and previous output with elasticities of 0.018, 0.01, 0.004 and 0.52 respectively. Real
price of rice and area cultivated are significant 10% level of significance while rainfall and lagged output
are significant 5%. In the long run aggregate output was found to be dependent on acreage cultivated and
the real price of rice and real price maize with elasticities of 0.218, 0.242 and -0.01 respectively at the 1%
significance level.
Mythili (2008) used panel data to estimate the supply response of Indian farmers. His findings also support
the view that farmers’ response to price is low in the short-run and their adjustment to reaching desired
levels is low for food grains.
The analysis showed that short-run response in rice production is lower than long-run response as indicated
by the higher long-run elasticities. This is because in the short run the farmers are constrained by the lack of
resources needed to respond appropriately to incentives. In the short-run inputs such as land, labour, and
capital are fixed. To address these concerns government should devise policies to make land available to
farmers so that prospective farmers could increase acreage cultivated. More irrigation facilities should be
constructed to put more land under cultivation.
It was also observed that the acreage model had higher elasticities than the output model. Thus farmers tend
to increase acreage cultivated in response to incentives. This implies that farmers have more control over
land than the other factors that influence output.
Efforts should be put in place to make the acquisition of inputs such as tractors and fertilizer more
accessible and affordable to farmers, and to improve the road network linking farming communities and the
urban centres.
Price control policy should be introduced and enforced to address the problem of frequent price fluctuation
which is the main reason for the low response to prices. Since farmers are aware of these price fluctuations
they are reluctant to immediately respond positively to price rises.
Though there is a market for rice in Ghana, recent developments have shown that consumers prefer foreign
polished rice; therefore, government should put in place the needed infrastructure to process the locally
produced rice to ensure the sustainability of local rice production.

References

Box G. E. P. and Jenkins G. M. (1976). ‘Time Series Analysis: Forecasting and Control’, 2nd ed, San
Francisco, Holden-day. Cambridge University Press.

Chinyere G. O. (2009). ‘Rice output supply response to changes in real prices in Nigeria; An Autoregressive
Distributed Lag Model approach’. Journal of Sustainable Development in Africa, 11(4), 83 -100. Clarion
University of Pennsylvania, Clarion, Pennsylvania.

Davidson, J. E. H., Handry, D. F., Srba F., and Yeo S. (1978). ‘Econometric modeling of aggregate time
series relationships between consumer’s expenditures and income in the UK’, Economic Journal. 88,
661-692.
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Dickey D. A. and Fuller W. A, (1981). ‘Likelihood ratio statistics for autoregressive time series with a unit
root’. Econometrica, 49, 1057-1072.

Engle R. F, and Granger C. W. J. (1987). ‘Cointegration and error correction: Representation, estimation
and testing’, Econometrica, 55, 251 -276.

FAO (1996). ‘Report of expert consultation on the technological evolution and impact for sustainable rice
production in Asia and Pacific’. FAO-RAP Publication No. 1997/23, 206 pp. Regional Office for the Asia
and the Pacific.

Granger C. W. J. (1981). ‘Some properties of time series data and their use in econometric model
specification.’ Journal of Econometrics, 16 (1), 121-130.

Granger C. W. J. and Newbold P. (1974). ‘Spurious Regression in Econometrics’, Journal of Econometrics
2, 111-120.

Ghana Statistical Service (2011). ‘Ghana Statistical Service release’.

Hall S. G. (1991). ‘The effect of varying length VAR on the maximum likelihood estimates of
co-integrating vectors.’ Scottish Journal of Political Economy, 38, 317- 323.

Hallam D. and Zanoli R. (1993). ‘Error Correction Models and Agricultural Supply Response’. European
Review of Agricultural Economics, 20, 151-166.

Hansen S. and Juselius K. (1995). ‘Cats in rats: Cointegration analysis of time series, Evanston, Illinois.

Harris R, (1995). Using cointgration analysis in econometric modeling. Prentice Hall, Harvester
Wheatsheaf, England.

Johansen S. and Juselius K. (1990). ‘Maximum likelihood estimation and inference on cointegration- with
application to the demand for money’. Oxford bulletin of Economics and statistics, 52, 170-209.

Kherallah M C., Delgado E., Gabri-Madhin N. M., and Johnson M., (2000). ‘The Road Half Travelled:
Agricultural Market Reform in Sub-Saharan Africa’. International Food Policy Research Institute, Food
Policy Report 10. Washington D.C. Kiel Working Paper No. 1016.

Krueger A. O., Schiff M, and Valdés A. (1992). ‘The Political Economy of Agricultural Pricing Policies’. A
World Bank Comparative Study, Baltimore.

Lewis W. A, (1954). ‘Economic Development with Unlimited Supplies of Labour’. Manchester School 22:
139–191.

Ministry of Food and Agriculture, Ghana (2005), Ministry of Food and Agriculture report.

Mohammed S. (2005). ‘Supply response analysis of major crops in different agro-ecological zones in
Punjab.’ Pakistan Journal of Agricultural Research, 2007, 124 (54).

Mythili G. (2008). ‘Acreage and yield response for major crops in the pre – and post reform periods in India:
A dynamic panel data approach’, Report prepared for IGIDR-ERS/USDA project: Agricultural markets and
policy, Indira Gandhi Institute of Development Research, Mumbai.

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Nerlove M. and Bachman K L., (1960). ‘Analysis of the changes in agricultural supply, Problems and
Approaches’. Journal of farm economics. 3(XLII), 531-554.

National Rice Policy Development (2009). National Rice Policy Development Report.

Statistical Research and Information Division (SRID). (2005). Statistics Research and Information
Department Release.

Thiele R., (2000). ‘Estimating the aggregate supply response, a survey of techniques and results for
developing countries.’ Kiel Institute of world Economics Duuesternbrooker Weg 120 241015 Kiel, Germany.
Kiel working paper No 1016 Rainer Thiele.

Thiele R, and Wiebelt M, (2000). ‘Adjustment lending in Sub Saharan Africa Kiel Institute of World
Economics.’ Kiel Discussion Papers 357, Kiel. Tobacco In Zimbabwe Contributed Paper Presented at the
41st Annual.

Townsend R and Thirtle C. (1994). ‘Dynamic acreage response. An error correction model for maize and
tobacco in Zimbabwe’. Occasional paper number 7 of the International Association Agricultural
Economics.

Townsend T. P. (2001). "World Cotton Market Conditions". Beltwide Cotton Conferences, Proceedings,
Cotton Economics and Marketing Conference, National Cotton Council, Memphis, TN, pp. 401-405.

World Bank. (1997). "Cotton and Textile Industries: Reforming to Compete." Vol. I, II, Rural Development
Sector Unit; South Asian Region, World Bank. Report No. 16347- IN, Washington, D.C, 1997.




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Sustainable Use of Water Resources in the Form of Pisciculture
     to Generate Income in West Bengal -A study report.
                                             Bairagya Ramsundar
                              Department of Economics, SambhuNath College,
                             Labpur, Birbhum, West Bengal, India, Pin-731303
       Mobile No. +919474308362 Fax: +913463 266255, E-mail: ramsundarbairagya@gmail.com

Received: October 1st, 2011
Accepted: October 11th, 2011
Published: October 30th, 2011


Abstract
Though the two-third of earth’s volume is comprised of water the world is facing the problem of scarcity of
fresh- water. Because most of the water is sea water which is salt in nature. It is the modern day tragedy that
due to the scarcity of these valuable lives saving natural resources life will exist no more. So the sustainable
use of this resource is very much important today. India was blessed with vast inland natural water
resources. But Indian Economy faces the problem of proper utilization of these huge water resources
spread over its vast stretches of land. A proper policy for utilization of these resources would become a
governing direction of economic growth. The role of fisheries in the country’s economic development is
amply evident. It generates employment, reduces poverty, generates income, increases food supply and
maintains ecological balance between flora and fauna. Scientists have shown that 3 bighas forest areas are
equivalent to 1 bigha plank origin in water bodies which create more O2. This paper intends to develop a
scientific plan use of water with a view to sustainable management of water resources to generate income
from fishery in the field of pisciculture by using the scarce water resources in a sustainable eco-friendly
manner.

Keywords: Ecology, Growth, Perishable, Pisciculture, Pollution, Project, Sustainable development,
Composite fish culture



1. Introduction



     According to 2001 Census India is the second (next to China) largest populous country in the world
about 17.5% of world populations live in India which covers only 2.4% (Dutt R. and Sundharam K.P.M.
2009) of geographical land area. The density of population is 324 persons per sq. km. If the current growth
rate of population (i.e. 2.11% per annum) continues within 2030 India will be the most populous country in
the world creating a food scarcity and a huge amount of unemployment in a massive scale. About 68% of
total population spends their livelihood from agriculture and the per capita income is very low. Water
scarcity is too much related to food scarcity. Hence it is very crucial to develop a scientific plan of water
use with a view to sustainable management of water resource. Management of shortage of water and
management of water pollution are complex task. These issues have drawn the attention of the developed
and developing countries as well as the various national and international organizations including the
Johannesburg Summit 2002, Year 2003 has been declared as year of Fresh -Water. But in India there are
huge prospect to utilize the unused fresh water resources for pisciculture which can play an important role
in this aspect. Realizing its importance during the 5th - five year plan the Government of India introduced
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beneficiary-oriented programme in the form of a pilot project entitled ‘Fish Farmers Development Agency’
(FFDA) to provide self employment, financial, technical and extension support to fish farming in rural
areas. In 1974-75 this Programme was further extended under World Bank-assisted, Inland fisheries project
to cover about 200 districts of various states in India.



    2. Sustainable development and Pisciculture



    Sustainable development is a pattern of resource use that aims to meet human needs while preserving
the environment so that these needs can be met not only in the present, but also for future generations. The
term was used by the Brundtland Commission which coined what has become the most often-quoted
definition of sustainable development as development that “meets the needs of the present without
compromising the ability of future generations to meet their own needs”. It is usually noted that this
requires the reconciliation of environmental, social and economic demands - the “three pillars” of
sustainability. This view has been expressed as an illustration using three overlapping ellipses indicating
that the three pillars of sustainability are not mutually exclusive and can be mutually
reinforcing. Sustainable development ties together concern for the carrying capacity of natural systems with
the social challenges facing humanity. As early as the 1970s “sustainability” was employed to describe an
economy in equilibrium with basic ecological support systems [Wikipedia]. A primary goal of sustainable
development is to achieve a reasonable and equitable distributed level of economic well being that can be
perpetuated continually for next generation. Thus the field of sustainable development can be broken into
three constituent parts i.e. environmental, economic and social sustainability. It is proved that socio-
economic sustainability is depended on environmental sustainability because the socio- economic aspects,
like agriculture, transport, settlement, and other demographic factors are born and raised up in the
environmental system. All the environmental set up is depended on a piece of land where it exists.



     Water is a renewable natural resource and is a free gift of nature. In the early days the supply
of water was plenty in relation to its demand and the price of water was very low or even zero but in course
of time the scenario has totally changed (Bairagya R. and Bairagya H. 2011). Water is a prime need for
human survival and also an essential input for the development of the nation. For sustainable development
management of water resources is very important today. Due to rapid growth of population, expansion of
industries, rapid urbanization etc. the demand of water rises many-fold which is unbearable in relation to its
resources in the earth. All the environmental set up is depended on a piece of land where it exists. So, to get
sustainable environmental management, sustainable land management is necessary. As a result the use of
water resources in the form pisciculture in a sustainable manner may gain priority.



   4. Importance of the study



    Social scientists have always identified the rural areas for investigation. In the case of India to a large
number of studies have been carried out in rural situations including panchayats and co-operative societies.
Though many research works have been done in the biological and marine sciences, the economic
investigations of pisciculture have not yet been done so far. In this respect the present study has a clear
economic importance for the upliftment of the rural economy at the grass root level. Thus pisciculture has a
positive effect on ecology and hence to maintain ecological balance a meaningful use of unused water
resources has an important role. Scarcity of water is a modern day tragedy human society will exist no more
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due to these scarce resources. So a meaningful use of this resources in the form of pisciculture to generate
income of the rural poor gain topmost priority.



The state of West Bengal plays an important role for the implementation of the programme. Though the two
districts Burdwan and Birbhum of W.B. are primarily agricultural districts, there is huge scope for
pisciculture. A few studies have been undertaken by several experts, notably, D. Prasad (1968), R. Charan
(1981), A.V. Natarajan (1985), K. M. B. Rahim (1992,93), A Chakravorty (1996), I Guha and R. Neogy
(1996), P.K. Ghosh (1998) and others on the economic evaluation of pisciculture. Though these are useful
guides to researchers, yet there is ample scope for further works relating to pisciculture in the rural areas of
W.B. Besides, there is the necessity of developing studies concerning the impact of these programmes on
the rural economy. The present study is a modest attempt in remedying this inadequacy.



4.1 Objectives



This pilot study was conducted on the following objectives i) To generate employment in rural India. ii) To
rise in food supply and reduce mal-nutrition. iii) For proper utilisation of unused water resources. iv) To
maintain ecological balance in a sustainable manner. V) Identification of fish farmers suitable and willing
to develop fish farming in ponds.vi) Arranging training in organized manner and ensuring extension
services to fish farmers.



4.2 Methodology of the study



4.2.1 Selection of study area



The present study was confined to survey the rural areas of Burdwan and Birbhum districts of W. B. in
respect of implementation of the programme of Fish Farmers Development Agency (Mishra S. 1987). In
the selection of districts following considerations weighed most: i) Both the districts are covered with water
areas constituting half of the total inland water resources. ii) There is a heavy concentration of tanks and
ponds in both the districts. iii) Both the districts are considered as having one homogeneous agro-climatic
zone in view of the broad similarities of soils, climate and other features. Since they are also neighboring
districts a suitable comparison can easily be made. iv) In both the districts, the FFDA programmes are being
implemented in full fledged form by the Government authority. v) Data from both the districts can be
obtained because of my personal knowledge about the two districts.



4.2.2 Selection of sample



Keeping in view the time factor, limited fund and limited ability it was not possible to collect data from all
the recorded fish farmers of the two districts. At first 5 blocks from each district have been purposefully
selected. Then 10 recorded fish farmers from each block have been selected purposefully. Thus 50 fish
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farmers from each district have been selected for interview. For comparative analysis of data each farmer
was taken as the unit. For this study data have been collected on different aspects of the programme such as
farmers’ income, water area, finance, total production, product price, cost of production, profit, duration of
training period etc. The data have been collected by personal interview method through a questionnaire.
Thus the collected data are entirely primary in nature.



4.2.3 Location map of the study area



The state of West Bengal lies between latitude 21038/ to 27010/ North and longitude 85038/ to 89050/ East.
The district Burdwan lies between 22056/ and 25053/ North latitude and 86048/ and 88025/ East longitude.
The district Birbhum lies between 23032/30// and 24035/00// North Latitudes and 88001/40// and 87005/25//
East Longitudes shown in figure-1.



5. Fisheries in India



Indian fisheries can broadly be divided into two categories: i) Inland fisheries & ii) Marine fisheries.
Further Inland fisheries can be classified into two types: i) Capture & ii) Culture. Capture fisheries consist
of rivers, lakes canals etc. where farmers do not cultivate fishes. Natural breeding process is the common
phenomenon there. On the other hand, culture fisheries consist of ponds, tanks, swamps, marshes etc. In
this case, farmers have to sow fish seeds, nurse it and send it to proper size before harvesting. India is the
third largest producer of fish in the world and the second in inland fish production. Indian fishing resources
comprising of 2 million sq.km.of EEZ for deep sea fishing, 7,520 km. of coast line, 29,000 km. of Rivers,
1.7 million Ha. of reservoirs, 1 million Ha. of brackish water and .8 million Ha. of ponds, lakes and tanks
for inland and marine fish production (Giriappa S. (ed) 1994) . About 14 million fishermen draw their
livelihood from fishery. During the period 1981 to 2002 the contribution of fishery to GDP has increased
from Rs.1230 crores to Rs.32060 crores. The fish production increased from 0.7 million tons in1951 to 6.8
million tons in 2006.



5.1 Fisheries in West Bengal



West Bengal has a vast water resource potentiality. By utilizing these water resources there is a huge
prospect of pisciculture (Chakraborty S. K.1991). These resources can be divided into two categories: i)
Inland water resources and ii) Marine water resources. Inland resources constitute ponds, rivers, marshy
lands, canals, reservoirs etc. Water Resources of West Bengal shown in figure-2.



It should be noted that tanks/ponds occupy the major share i.e. 46.70% of total inland water resources. But
out of 2, 76,202 Ha. area under ponds and tanks only 2, 20,000 Ha. i.e. 79.65% are presently used for
pisciculture which means 20.35% remains unused. Moreover, out of 5, 91,476.71 Ha. total inland water
resource only 2.87000 Ha. water area is brought under pisciculture i.e. 48.56% are presently used and
51.44% remains unused. These unused water resources can be brought under pisciculture through proper
utilization.
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In West Bengal marine fishery has a substantial share amounting to a coast line of 158 km. inshore area up
to 10 fathoms depth is 770 sq. km. (Mamoria C.B. 1979), offshore area (10-40) fathoms depth is 1813 sq.
km. and a continental shelf up to 100 Fathom is 17,049 sq.km. Out of 19 districts of West Bengal only two
districts East- Midnapur and South 24-Parganas are coastal. West Bengal is on the top of the list in fish
production in the country. With the passage of time, more and more people are getting themselves involved
in fishery. As fish constitutes the staple food of the people efforts are being made to augment fish
production.



From the period 1986 to 2000, the total fish production increased from 424000 tons to 1045,000 tons (i.e.
2.46 times). At the same period the inland fish production increased 2.25 times and marine fish production
increased 4.50 times. In the year 1986, the share of inland and marine fisheries to total fish production were
91% and 9% respectively. But in the year 2000, the share of inland and marine fisheries to total fish
production are 83% and 17% respectively. Thus we see that during the period 1986 to 2000, the share of
inland fish to total production declined (from 91% in 1986 to 83% in 2000) and the share of marine
fisheries increased (from 9% in 1986 to 17% in 2000).



Not only in fish production but also in the demand for fish West Bengal is the highest in the country. The
domestic demand for fish in West Bengal is high because almost all the people of West Bengal are
fish-eating. But the state has a higher demand for fish than its production of fish i.e. this state has a deficit
in fish supply. To meet this gap the state West Bengal has to import fish from other states like Andhra
Pradesh, Tamil Nadu etc. At the same time various efforts have been made to augment fish production to
bridge this gap.



5.2 Fisheries in Burdwan district



The district of Burdwan is mainly an agricultural district though it is also well advanced in industrial
production. It is called the ‘granary’ of West Bengal. The district is filled with well fertile productive land.
Not only that, the district is also well endowed with a large number of water bodies in the form of rivers,
ditches, canals, marshy lands, ponds, tanks etc. The principal rivers are Ajoy, Damodar and Kunur etc.
There is a good prospect of pisciculture by utilizing these water resources. The distribution of total water
resources in Burdwan district (i.e. 66480.82 Ha.) is shown in figure-3.



Though the district has huge water resources, during the period 1982-1998 only 50% of this water area was
brought under scientific pisciculture. In the year 1999, the district’s total fish production was 55,500 metric
tons while demand in that year was 65,000 metric tons. Thus there was a deficit of 9,500 metric tons. To
meet this demand the district had to import fish from neighboring states. The district will be self-sufficient
in fish production if the unused 50% water resources are brought under pisciculture.



5.3 Fisheries in Birbhum district




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The district of Birbhum is a part of Rarh area. The district is well-drained by a number of rivers and rivulets.
The principal rivers of this district are: Ajoy, Brahmani, Dwarka, Kopai, Mayurakshi etc. But most of the
rivers remain dried up during a greater part of the year. Due to this adverse natural factor pisciculture has not
made any significant progress in this district. Agriculture is the main occupation of the common people of
this district. The main water areas for pisciculture of this district are rivers, tanks, ponds, khals, Bills, baors,
reservoirs etc. The distribution of total water resources in Birbhum district (i.e. 45215.81 Ha.) is shown in
figure-4.



The figure-4 shows that this district has a large number of water resources. But during the period 1980-1999,
only 46% of this water area was brought under pisciculture through FFDA. So there is a huge prospect of
pisciculture by utilizing these unused water resources. In the year 1999 total number of fish farmers in this
district was 2, 00747 out of which 56.5% were males and 43.5% were females. The total number of fisherman
family in this district was 4,975. The average fish production of this district was 21,000 metric tons and the
annual demand was 24,000 metric tons. Thus the district had a deficit (3000 mt) in fish production. To meet
its own demand the district has also to import fish from the neighbouring states.



6. Findings



6.1 Income distribution of sample fish farmers before and after assistance from FFDA



It was observed that 26 farmers in Burdwan district & 32 farmers in Birbhum district have pisciculture as
the main occupation while others have pisciculture as a subsidiary occupation. Their basic occupations are
agriculture, business and service and have various types of incomes from those occupations. But as income
generation scheme only income from fishery was taken into account i.e. income means income earned from
selling fishes. From sample survey we have collected two sets of income data (one showing income before
and other showing income after the assistance of FFDA) for each fish farmer. Thus the amount of income
reveals the income of the fish farmer before the receipt of assistance of FFDA and the income after the
assistance of FFDA. In this regard the income comparison has done in two areas (namely within the
district, between the district and the overall change of income) and‘t’ (Gujarati D.N. 2009) test was used for
statistical analysis. Now the various types of incomes distribution of fish farmers of the two districts are
shown in terms of four tables.



From table- 1 it is clear in both the districts the average income of most of the fish farmers (i.e. 89 out of
100 fish farmers of the two districts taken together) has monthly income before assistance below Rs. 4000
i.e. they are basically come from poor family income group



  i) From the field survey we get that the per capita availability of fish in Burdwan district is 24 kg. per head
per year while in Birbhum district it is 23.5 kg. per head per year. Now the world’s per capita availability of
fish is 11.5 kg. per head per year and for developed countries it is 25 kg. per head per year. But India has a
low per capita availability of 3.5 kg. per head per year only. Thus we see that the fishermen have a standard
level of per capita fish consumption. The point is very important to reduce mal-nutrition (which is a
common phenomenon for the country) by means of intensive pisciculture. But India has a poor per capita

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availability of fish of only 3.5 kg per head per year. Thus we see that the per capita availability of fish for
these 100 farmers’ families of these two districts are much higher than that of India and world’s average
and even equal to the standard of the developed countries.



ii) It was found that the average monthly income of fish farmers before the assistance of FFDA in Burdwan
district was Rs. 2680 and in Birbhum district it was Rs. 2045. But after the assistance of FFDA the average
monthly incomes of the fish farmers of the two districts increased to Rs.3935 and Rs.3721 respectively.
Thus we see that the average monthly income of fish farmers of Burdwan district has increased 1.47 times
and in case of Birbhum district it has increased 1.8 times. The result indicates that the average income of
fish farmers of Burdwan district was higher than the average income of those of Birbhum district in respect
of both before and after the assistance of FFDA. However, the rate of increment was higher in Birbhum
than that of Burdwan district. Thus we can say that the incomes of the fish farmers have improved due to
pisciculture.



 iii) Statistical methods were adopted is to judge whether the change of income of the sample fish farmers
before and after the assistance of FFDA was significant or not. This has been done in two areas: - a) Change
of income within the district and b) Overall change of income of the two districts.



6.2 Change of income within the district



Burdwan district- It is seen that the value of‘t’ = 3.31 is significant at .01 level, meaning thereby that the
change income of sample fish farmers of Burdwan district is significant. The gain is in favor of the
assistance of FFDA. It may be concluded from the result that there is a positive impact of assistance on fish
farming (i.e. the production of fish). It may also be concluded that production has increased significantly in
Burdwan district.



Birbhum district-The result indicates that “t” is significant at .01 level. Hence it may be said that in
Birbhum district the change of income of fish farmers is significant.



                       6.3 Overall change of income taking the two districts together



It is seen from the above table that the value of‘t’ is significant at .01 level which indicates that the income
of fish farmers before and after assistance differ significantly. The result reveals that the mean income after
 assistance is significantly greater than that before assistance. It may be concluded from the overall results
         considering both the districts that the impact of assistance on fish production is significant.



The finding of these results was that the assistance of FFDA programme has raised the fish farmer’s income
undoubtedly in both the districts. Not only that the change of income was statistically significant but also
the result revealed that there was a positive impact of FFDA assistance on fish production (i.e. fish
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production has significantly increased). Moreover, it was also found that the gain of income of fish farmers
of Birbhum district was higher than that of Burdwan district. Thus we can conclude that in case of the
implementation of the programme of FFDA the district Burdwan has more successful achievements than
the district of Birbhum.



7 Suggestions



7.1 General suggestions



i) In pisciculture, fishermen are not only directly employed in fishing but also some other alternative
occupations like net making, marketing of fish seed and fishery product , transport, boat making etc. many
rural people earn income. Since fish is a perishable commodity proper marketing channels should be
established. Hence to reduce pressure from agriculture pisciculture may be alternative occupations for
generating income and employment for a large number of poor people. ii) In Burdwan district there are
some open caste pits (OCP) in Ranigang coal belt and in Birbhum district also such pits are available at
Khoirasole block, calamines at Md.Bazar block. Fish production may increase by utilizing these water
resources for pisciculture purposes.iii) The urban waste (i.e. garbage) may be recycled as fish feed (to those
ponds and water areas lying near the towns) to raise fish production and to prevent the environmental
pollution in those areas. v) To make financial support for the poor fishermen Bank should grant loan on a
long-term basis and at a low rate of interest in proper amount and in proper time.vi) the selection of actual
beneficiary is very much essential and it should be made on the basis of need and neutrally not in politically.
vii) Since fish is a perishable commodity storage facilities should be provided such that the fishermen are
not forced to sell their product at a lower price. Prices of organic manures and fish seeds should be kept as
low as possible or the Government should give more subsidies in the case of chemical fertilizers so that the
poor fish farmers can buy them. viii) For welfare measures of the rural poor fishermen some Group and
Personal Insurance Schemes, Old- Age Pension Schemes should be taken and the Fishery Dept. should
issue “Identity Card” to each fisherman. ix) Since both the districts are agricultural based we should
interlink agriculture with pisciculture shown in figure-5. Along with pisciculture in ponds other allied
culture can be inter-linked in composite farming. The concept of composite farming e.g. in the pond- there
are pisciculture and on one side of the pond mulberry trees can be cultivated for the development of
sericulture industry-from there silk industry can be grown. Thus the final products (i.e. silk yarn and silk
cloth) come to the market and their waste materials are drained off to the pond and used as fish feed. In the
same pattern on another side of the pond animal husbandry can be practiced (e.g. poultry, duccary and
piggery). Their waste can be used as a valuable manure for fish feed and the residual can be utilized for
agricultural production. The excreta of the animal husbandry can also be used in the bio-gas plant for fuel
and light. The products of animal husbandry e.g. milk, meat and egg come to the market directly. On
another side of the pond some fruit plants such as papine, guava, mango etc. can be cultivated by using the
excess manure of animal husbandry and the products can be sold in the market.



x) Composite fish culture: Stocking of various species should be in a certain proportion such that various
types of fishes live in various layers and eat the entire food organism (so called Polyculture or Composite
Fish Culture). Stocking is an important factor to raise fish production. On an average the survibility of
stocking is 80% (Jhingran V.G. 1991). This means that 20% of stocking is lost for various reasons such as
improper handling, netting and also eaten by preratory animals like snakes, frogs etc. The main species
cultured in India are Rahu, Catla, Mrigel, Silver Carp (S.C.), Grass Carp (G.C.), Cyprinus Carpio (Cy, Ca.)

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and Bata etc. In polyculture (where various fishes are cultured simultaneously) (Agarwal S.C. 1990) system
all these species are cultured in certain ratios. It is found that all of these species do not generally live in the
same layer. In mixed (or polyculture or composite culture) culture all these fishes are cultured in such a way
that all layer’s feed are used. If we divide the whole layer into 3 parts i.e. upper, middle and lower layers,
then Cattla and S.C live in the upper layer, Rahu and G.C. live in the middle layer and Mrigel and Cy. Ca.
live in the lower layer. It is also to be noted that these two species in the same layer are not competitors of
each other. In their feed habit one eats waste weeds and the other eats green weeds. Hence in polyculture all
the pond’s feed are used scientifically and economically. The production of Big head and Big head African
giant hybrid magur are totally banned because their food habits are too much and hamper the foods of other
common species. Suppose 1000 fish seed is cultivated in the pond. Then the stocking ratios for various
fishes are Rahu: Catla: Mrigel: S.C.: G.C.: Cy.Car = 3 : 1 : 1.5 : 1.5 : 1 : 2 Thus the number of seeds are,
Rahu = 300, Catla = 100, Mrigel = 150, S.C = 150, G.C. = 100, Cy. Ca. = 200. Now the survibility rate is
80%.Thus the number of produced matured fishes are Rahu = 240, Catla = 80, Mrigel = 120, S.C. = 120,
G.C. = 80, Cy.Car. = 160. Fishes are cultivated for 9-12 months. The culture period may extend up to 18
months with partial harvesting between. Moreover, the general annual growth of these fishes are, Rahu =
1kg, Catla 1.5 kg. Mrigel 0.75kg, SC. = 0.8 kg, G.C. = 1.5 kg, Cy. Car. = 0.8 kg. The fish farmers collect
fish seeds from private traders or from Government fish farm.



xi) Since training is a necessary ingredient to raise fish production the Government has initiated some
special training programmes for fish farmers. Moreover to motivate the poor fish farmers some amount of
remuneration or stipend is paid to the participant during training. The amount of remuneration depends
upon the kind of training, place of training organization and duration of the training programmes conducted
by the FFDA. To attend the district level training programmes for the farmers of distant villages, traveling
allowances are also paid.



xii) It is very essential to use mahua cake because it has dual roles in pisciculture. It firstly acts as a
pesticide but later it acts as organic manure and produces a desired quantity of fish food organism for the
baby fishes. The presence of ‘Saponin’ in mahua cake kills the pre-ratory animals / fishes of the pond. This
poisonous effect continues about 10-15 days and after that it acts as manure. On the other hand, the use of
pesticides has a negative long-term effect to ecological balance. So the pesticides are not generally used
though they are cheaper. The mahua cake should be applied at the rate of 333.33kg/ Yr. / bigha.



7.2 Limitations of the study



The following are the limitations of the study: i) Regarding the change in the level of income before and
after the assistance of FFDA, the statements of the fish farmers have been taken into consideration on good
faith. ii) Due to limited time, ability and resource constraints, data have been collected from a small number
of fish farmers of the two districts and results are assumed to be the representative for the district as a
whole. Iii) Due to difficulties in getting responses from the sample fish farmers sometimes we had to rely
on the different FEOs’ opinions and also on different official records on good faith. iv) The observations
made on the basis of collected data are obviously particularistic in nature in so far as these data relate to
micro-study like the present one (i.e. only 100 fish farmers from the two districts were taken into
consideration). Micro-studies do not attempt to build general theories but the utility of this type of study is
that large number micro-studies may, in course of time, be helpful in constructing meaningful
generalizations. Moreover, it is an explorative study which seeks to explore the conditions of fish farmers

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before and after the assistance of FFDA programme. A much larger study may be undertaken to vindicate
the results obtained from this explorative study.



8. Conclusion



The study indicates that the introduction of the programme of FFDA had a clear positive impact on the rural
economy through employment and income generation and also raising the standard of living and
socio-economic performances of the rural community of the two districts. It is environmentally viable,
sustainable and eco-friendly in nature. So for sustainable use of the scarce water resources it is very
essential in the present context. Unemployment is a curse for the society today and pressure of population
on agricultural sector is rising day by day. Thus fish farming may be an alternative occupation for those
unemployed people and undoubtedly generate income in a viable method. Therefore it is recommended that
the present programme should be further spread in the rural areas by means of proper planning, adequate
supervision, effective implementation and better monitoring in a sustainable manner.



References:



Agarwal S.C. (1990), Fishery Management, Ashis Publishing House, New-Delhi.

         Bairagya R. and Bairagya H. (2011), “Water Scarcity a Global Problem- An Economic Analysis”,
Indian Journal of Landscape Systems and Ecological Studies, Vol.-34, Institute of Landscape, Ecology &
Ekistics, Kolkata, 127-132

       Bairagya R. (2004), “A Comparative Study of the Functioning of Fish Farmers Development
Agency (FFDA) in the Districts of Burdwan and Birbhum (1985-1995)”, PhD Thesis, Department of
Commerce, the University of Burdwan.

        Bureau of Applied Economics, Burdwan (1998), Key Statistics of Burdwan, Govt. of W.B.

Bureau of Applied Economics, Birbhum (1998), Key Statistics of Birbhum, Govt. of W.B

Chakraborty S. K. (1991), A Study in Bhery Fisheries, Agro-Economic Reaserch Centre, Visva- Bharati.

Dutt R. and Sundharam K.P.M. (2009), Indian Economy, S. Chand and Co. New-Delhi, 101-103

Jhingran V.G. (1991), Fish and Fisheries in India, Hindustan Publishing Cor.-Delhi.

Giriappa S. (ed) (1994), Role of Fisheries in Rural Development, Daya Publishing House, Delhi.

        Gujarati D.N. (2009), Basic Econometrics, McGraw-Hill., 98-133

Mamoria C.B. (1979), Economic & Commercial Geography of India, Shiblal Agarwala Company, Agra-3

Mishra S. (1987), Fisheries in India, Ashis Publishing House, New-Delhi

Acknowledgement: In preparation of this paper I deeply acknowledge my respected sir Prof. Jaydeb
Sarkhel, Department of Commerce, Burdwan University, West Bengal, India, e-mail:

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jaydebsarkhel@gmail.com




                                      Tables and Figures:




                                           Figure-1



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                                      Figure-2




Figure-3                                                  Figure-4

Table -1 Change of income distribution of Burdwan.




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                               Table -2 Change of income distribution of Birbhum district




Table--3 Classification of sample fish farmers according to their monthly incomes after the assistance




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  Table- 4 Classification of sample fish farmers according to their monthly incomes after the assistance




                          Source: All tables data sources are computed from field survey.




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                                                    MARKET
                                                                                         Light, Fuel


                 Silk yarn &                                                             Gobar gas
                 Silk cloth

                                                                                    Milk, Meat & Egg
                Silk industry

                                                                                    Cow, Piggary,
                                                              waste materials
                                                                                    Duccary, Poultry
                                w
                                 as



                Sericulture
                                   te
                                    m
                                     at
                                       er
                                        ia



                                                                                  Agricultural production
                                           ls



                                                  Produced Fish                   (Fruit, papaine, guava,
               Mulburyculture
                                                                                  mango etc.)



                    Side                              POND                                   Side



                                Circular flow of pisciculture and other allied culture

                                                       Figure: 5




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  • 1. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Supply Response of Rice in Ghana: A Co-integration Analysis John K.M. Kuwornu (Corresponding author) Department of Agricultural Economics and Agribusiness, P. O. Box LG 68, University of Ghana, Legon-Accra, Ghana Tel: +233 245 131 807 E-mail: jkuwornu@ug.edu.gh / jkuwornu@gmail.com Maanikuu P. M. Izideen Department of Agricultural Economics and Agribusiness, P. O. Box LG 68, University of Ghana, Legon-Accra, Ghana Tel: +233 241 913 985 E-mail: maanikuu@yahoo.com Yaw B. Osei-Asare Department of Agricultural Economics and Agribusiness, P. O. Box LG 68, University of Ghana, Legon-Accra, Ghana Tel: +233 209 543 766 E-mail: yosei@ug.edu.gh Received: October 1st, 2011 Accepted: October 11th, 2011 Published: October 30th, 2011 Abstract This study presents an analysis of the responsiveness of rice production in Ghana over the period 1970-2008. Annual time series data of aggregate output, total land area cultivated, yield, real prices of rice and maize, and rainfall were used for the analysis. The Augmented-Dickey Fuller test was used to test the stationarity of the individual series, and Johansen maximum likelihood criterion was used to estimate the short-run and long-run elasticities. The land area cultivated of rice was significantly dependent on output, rainfall, real price of maize and real price of rice. The elasticity of lagged output (12.8) in the short run was significant at 1%, but the long run elasticity (4.6) was not significant. Rainfall had an elasticity of 0.004 and significant at 10%. Real price of maize had negative coefficient of -0.011 and significant at 10% significance level. This is consistent with theory since a rise in maize price will pull resources away from rice production into maize production. The real price of rice had an elasticity of 2.01 and significant at 5% in the short run and an elasticity of 3.11 in the long run. The error correction term had the expected negative coefficient of -0.434 which is significant at 1%. It was found that in the long run only real prices of maize and rice were significant with elasticities of -0.46 and 3.11 respectively. The empirical results also revealed that the aggregate output of rice in the short run was found to be dependent on the acreage cultivated, the real prices of rice, rainfall and previous output with elasticities of 0.018, 0.01, 0.003 and 0.52 respectively. Real price of rice and area cultivated are significant 10% level of significance while rainfall and lagged output are significant 5%. In the long run aggregate output was found to be dependent on acreage cultivated, real price of rice, and real price maize with elasticities of 0.218, 0.242 and -0.01 respectively at the 1% significance level. The analysis showed that short-run responses in rice production are lower than long-run response as indicated by the higher long-run elasticities. These results have Agricultural policy implications for Ghana. Key Words: Supply response, Rice, Error Correction Model, Co-integration Analysis, Ghana 1. Introduction One of the most influential policy prescriptions for low-income countries ever given by development economists has been to foster industrialization by withdrawing resources from agriculture (e.g., Lewis, 1954). There is robust evidence that the majority of policy makers followed this prescription at least until the mid-1980s. The results of a comprehensive World Bank study (Krueger et al., 1992), for example, show for the period 1960 –1985 that in most countries examined, agriculture was taxed both directly via 1
  • 2. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 interventions in agricultural markets and indirectly via overvalued exchange rates and import substitution policies. It is not obvious whether the disincentives for agricultural production have continued to exist since the mid-1980s. On the one hand, most developing countries have adopted structural adjustment programs which explicitly aim at a removal of the direct and indirect discrimination against agriculture. But, on the other hand, it is known that many of these programs were not fully implemented, especially in Sub-Saharan Africa (Kherallah et al., 2000; Thiele and Wiebelt 2000; World Bank, 1997). Hence it can be concluded that a certain degree of discrimination still prevails. One of the most important issues in agricultural development economics is supply response of crops. This is because the responsiveness of farmers to economic incentives determines agricultures’ contribution to the economy especially where the sector is the largest employer of the labour force. This is often the case in third world low income countries. Agricultural pricing policy plays a key role in increasing farm production. Supply response is fundamental to an understanding of this price mechanism (Nerlove and Bachman, 1960). There is a notion that farmers in less developed countries respond slowly to economic incentives such as price and income. Reasons cited for poor response vary from factors such as constraints on irrigation and infrastructure to a lack of complementary agricultural policies. The importance of non-price factors drew adequate attention in the literature: rainfall, irrigation, market access for both inputs and output, and literacy. The reason cited for a low response to prices in less developed economies is the limited access to input and product markets or high transaction costs associated with their use. Limited market access may be either due to physical constraints such as absence of proper road links or the distances involved between the roads and the markets, or institutional constraints like the presence of intermediaries (Mythili, 2008). In Ghana, the goals of agricultural price policy are among others fair incomes for farmers, low food prices for urban consumers, cheap raw materials for manufacturing (Ministry of Food and Agriculture (MOFA), 2005). Price support was one policy that was used by government in targeting these goals. Price support has been used in many countries across the world. The prices of major commodities have been set below world prices using subsidies and trade barriers, guaranteed prices (act as floors) and domestic market forces determining actual prices (Food and Agriculture Organization (FAO), 1996). The effect of liberalization on the growth of agriculture crucially depends on how the farmers respond to various price incentives. For many low-income countries, the impact of structural reforms on economic growth and poverty alleviation crucially depends on the response of aggregate agricultural supply to changing incentives. Despite its policy relevance, the size of this parameter is still largely unknown (Thiele, 2000). Rice is a very important crop especially for those areas where it is produced. Rice is gradually taking the position as the main staple food for the majority of families in Ghana especially in the urban centres. The growth in consumption of rice has been not been matched with a corresponding growth in local production of the crop. Per capita consumption of rice has steadily increased over the years since the 1980s from about 12.4kg/person /year in 1984 to about 20kg/person /year (Statistical Research and Information Division (SRID), 2005). It continued to rise to 38kg/person/year in 2008 (National Rice Development Policy (NRDP), 2009). Over the last decade rice per capita consumption has increased by more than 35%. This is attributed mainly to the rapid urbanization and changes in food consumption patterns. It is estimated based on population and demand growth rates that per capita consumption will reach 41.1kg by 2010 and 63.0kg by 2015 giving an aggregate demand of 1,680,000tons/year (Statistical Research and Information Division (SRID), 2005). Changes in population dynamics and the taste or preference for foreign products is contributing to this trend. These changes are significant considering the rate of population growth. Ghana’s population has grown by about 70% from 12.3 million in 1984 to about 21 million in 2004. The population is now 24 million (Ghana Statistical Service (GSS), 2011). The growing quantum of rice imports into the country has also been triggered partly by the fall in world market price. This is largely due to increased output levels in India and South Asia. Massive subsidies in rice exporting countries have also contributed to this phenomenon. Despite all these developments there seem to be a lack of coherent and comprehensive national policy for rice in Ghana. From the period of economic recovery and structural adjustment, the country has had to embark on trade liberalization and the removal of all forms of subsidies on agriculture. These policies no doubt have adversely affected rice production in the country. The smallholder rural farmer is faced with unfair competition from abroad. The 2
  • 3. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 role of incentives to farmers has generally been sidelined or ignored in most developing countries due to conditions of trade liberalization and global trade integration. However, a number of empirical studies in other developing economies addressed the question of farmers’ response to economic incentives and efficient allocation of resources (e.g. Chinyere, 2009). The agricultural sector in Ghana has undergone various policy regimes which has affected both the factor and product market resulting in changes in the structure of market incentives (prices) faced by farmers. Most of these policies have, however, not been crop specific and therefore has wide variations in the quantum of changes in the incentives. This study therefore follows the supply response framework of analysis to examine the dynamics of the supply of rice in Ghana. Effort in this direction will have to be preceded by a thorough analysis of the factors that affects the supply of rice. These teething problems lead to the following research questions; How responsive is rice production to price and non-price factors? What are the long run and short run elasticities of rice production? What are the trends in area cultivated, output and real prices of output? Therefore, objective of the study is threefold: (a) Examine the acreage and output response of rice production in Ghana. (b) Estimate and compare the long run and short run elasticities of rice production. (c) Analyze the trends in output, area cultivated and real prices. The rest of the paper is structured as follows. Section 2 outlines the methodology. In section 3 we present the empirical applications and the results. Section 4 provides the conclusions. 2.0 Methodology This section presents the methodology of the study. 2.1 Linear Regression Linear regression was conducted to determine the growth rates of the variables over the study period. Time in years was regressed on acreage cultivated, aggregate output, real price of rice separately. 2.2 Time series analysis In empirical analysis using time series data it is important that the presence or absence of unit root is established. This is because contemporary econometrics has indicated that regression analysis using time series data with unit root produce spurious or invalid regression results (e.g. Townsend, 2001). Most time series are trended over time and regressions between trended series may produce significant parameters with high R2s, but may be spurious or meaningless (Granger and Newbold, 1974). When using the classical statistical inference to analyze time series data, the results are only stationary when the series are stationary. The solution to this problem was initially provided by Box and Jenkins (1976), by formulating regressions in which the variables were expressed in first difference. Their approach simply assumed that non-stationary data can be made stationary by repeated differencing until stationarity is achieved and then to perform the regression using these differenced variables. However, according to Davidson et al., (1978), this process of repeated differencing even though leads to stationarity of the series; it is achieved at the expense of losing valuable long run information. This posed a new challenge to time series econometrics. The concept of cointegration was introduced to solve these problems (Granger, 1981; Engle and Granger, 1987). By using the method of cointegration an equation can be specified in which all terms are stationary and so allow the use of classical statistical inference. It also retains information about the long run relationship between the levels of variables, which is captured in the stationary co-integrating vector. This vector will comprise the parameters of the long run equilibrium and corresponds to the parameters of the error correction term in the second stage regression (Mohammed, 2005). The cointegration approach takes into consideration the long-run information such that spurious results are avoided. 2.3 Stationarity Tests A data series is said to be stationary if it has a constant mean and variance. That is the series fluctuates around its mean value within a finite range and does not show any distinct trend over time. In a stationary series displacement over time does not alter the characteristics of a series in the sense that the probability distribution remains constant over time. A stationary series is thus a series in which the mean, variance and covariance remain constant over time or in other words do not change or fluctuate over time. In a stationary 3
  • 4. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 series the mean always has the tendency to return to its mean value and to fluctuate around it in a more or less constant range, while a non-stationary series has a changing mean at different points in time and its variance change with the sample size (Mohammed, 2005). The conditions of stationarity can be illustrated by the following: Yt = ѳYt-1 + µ t t=1………T (1) Where µ t is a random walk with mean zero and constant variance. If ѳ < 1, the series Yt stationary and if ѳ = 1 then the series Yt is non-stationary and is known as random walk. In other words the mean, variance and covariance of the series Yt changes with time or have an infinite range. However Yt can be made stationary by differencing. Differencing can be done multiple times on a series depending on the number of unit roots a series has. If a series becomes stationary after differencing d times, then the series contains d unit roots and hence integrated of order d denoted as I (d). Thus, in equation (1) where ѳ = 1, Yt has a unit root. A stationary series could also exhibit other properties such as when there are different kinds of time trends in the variable. The DF (Dickey-Fuller)-statistic used in testing for unit root is based on the assumption that µ t is white noise. If this assumption does not hold, it leads to autocorrelation in the residuals of the OLS regressions and this can make invalid the use of the DF-statistic for testing unit root. There are two approaches to solve this problem (Towsend, 2001). In the first instance the equations to be tested can be generalized. Secondly the DF-statistics can be adjusted. The most commonly used is the first approach which is the Augmented Dickey-Fuller (ADF) test. µ t is made white noise by adding lagged values of the dependent variable to the equations being tested, thus: ∆Yt = (ѳ1 – 1) Yt-1 + IYt-I + µ t (2) ∆Yt = α2 + (ѳ2 – 1) Yt-1 + I ∆Yt-I + µ t (3) ∆Yt= α3+β3t(ѳ3–1)Yt-1+ I ∆Yt-I + µ t (4) The ADF test uses the same critical values with DF. The results of the ADF test for unit roots for each of the data series used in this study are presented in in the next section using equation (4) where Yt is the series under investigation, t is the time trend, α3 is the constant term and µ t are white noise residuals. Eviews was used in the analysis, all the data series was tested for stationarity and the results are presented in section three. 2.4 Cointegration Cointegration is founded on the principle of identifying equilibrium or long run relationships between variables. If two data series have a long run equilibrium relationship it implies their divergence from the equilibrium are bounded, that is they move together and are cointegrated. Generally for two or more series to be co-integrated two conditions have to be met. One is that the series must all be integrated to the same order and secondly a linear combination of the variables exist which is integrated to an order lower than that of the individual series. If in a regression equation the variables become stationary after first differencing, that is I (1), then the error term from the cointegration regression is stationary, I (0) (Hansen and Juselius, 1995). If the cointegration regression is presented as: Yt = α + βXt + µ t (5) where Yt and Xt are both I (1) and the error term is I (0), then the series are co-integrated of order I (1,0 ) and β measures the equilibrium relationship between the series Yt and Xt and µ t is the deviation from the long-run equilibrium path. An equilibrium relationship between the variables implies that even though Yt and Xt series may have trends, or cyclical seasonal variations, the movement in one are matched by movements in the other. The concept of cointegration has implications for economists. The economic interpretation that is accepted is that if in the long-run two or more series Yt and Xt themselves are non-stationary, they will move together closely over time and the difference between them is constant (stationary) (Mohammed 2005). 2.4.1 Testing for Cointegration There are two most commonly used methods for testing cointegration. The Augmented Dickey-Fuller residual based test by Engle and Granger (1987), and the Johansen Full Information Maximum Likelihood (FIML) test (Johansen and Juselius, 1990). For the purpose of this study the Johansen Full Information Maximum Likelihood test is used due to its advantages. The major disadvantage of the residual based test is 4
  • 5. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 that it assumes a single co-integrating vector. But if the regression has more than one co-integrating vector this method becomes inappropriate (Johansen and Juselius, 1990). The Johansen method allows for all possible co-integrating relationships and allows the number of co-integrating vectors to be determined empirically. 2.4.2 Johansen Full Information Maximum Likelihood Approach The Johansen approach is based on the following Vector Autoregression Zt = AtZt-1 + … + AkZt-k + µ t (6) Where Zt is an (n×1) vector of I(1) variables (containing both endogenous and exogenous variables), At is (n×n) matrix of parameters and µ t is (n×1) vector of white noise errors. Zt is assumed to be nonstationary hence equation (6) can be rewritten in first difference or error correction form as; ∆Zt = Γ1∆Zt-1 + … + Γk-1∆Zt-k+1 + πZt-k + µ t (7) where Γ1 = - ( 1- A1 - A2 - … -Ai), (i = 1, …, k-1) and π = - (1- A1-A2- …-Ak). Γ1 gives the short run estimates while π gives the long run estimates. Information on the number of co-integrating relationships among variables in Zt is given by the rank of the matrix π. If the rank of π matrix r, is 0 < r > n, there are r linear combinations of the variables in Zt that are stationary. Thus π can be decomposed into two matrices α and β where α is the error correction term and measures the speed of adjustment in ∆Zt and β contains r co-integrating vectors, that is the cointegration relationship between non-stationary variables. If there are variables which are I(0) and are significant in the long run co-integrating space but affect the short run model then equation (7) can be rewritten as: ∆Zt = Γ1∆Zt-1 + πZt-k + vDt + µ t (8) where Dt represents the I(0) variables. To test for co-integrating vector two likelihood ratio (LR) tests are used. The first is the trace test statistic; Λtrace = -2lnQ = -T i) (9) Which test the null hypothesis of r co-integrating vectors against the alternative that it is greater than r. The second test is known as the maximal-eigen value test: Λmax = -2 ln(Q: r 1 r + 1 = -T ln(1-λr+1 ) (10) which test the null hypothesis of r co-integrating vectors against the alternative of r+1 co-integrating vectors. The trace test shows more robustness to both skewness and excess kurtosis in the residuals than the maximal eigen value test (Harris, 1995). The error correction formulation in (7) includes both the difference and level of the series hence there is no loss of long run relationship between variables which is a characteristic feature of error correction modeling. It should be noted that in using this method, the endogenous variables included in the Vector Autoregression (VAR) are all I(1), also the additional exogenous variables which explain the short run effect are I(0). The choice of lag length is also important and the Akaike Information Criterion (AIC), the Scharz Bayesian Criterion (SBC) and the Hannan-Quin Information Criterion (HQ) are used for the selection. According to Hall (1991) since the process might be sensitive to lag length, different lag orders should be used starting from an arbitrary high order. The correct order is where a restriction on the lag length is rejected and the results are consistent with theory. 2.5 Error Correction Models (ECMs) The idea behind the mechanism of error correction is that a proportion of disequilibrium from one period is corrected in the next period in an economic system (Engle and Granger, 1987). The process of making a data series stationary is either done by differencing or inclusion of a trend. A series that is made stationary by including a trend is trend stationary and a series that is made stationary by differencing is difference stationary. The process of transforming a data series into stationary series leads to loss of valuable long run information (Engle and Granger, 1987). Error correction models helps to solve this problem. The Granger representation theorem is the basis for the error correction model which indicates that if the variables are cointegrated, there is a long-run relationship between them and can be described by the error correction model. The following equation shows an ECM of agricultural supply response involving the variables Y and X in its simplest form: 5
  • 6. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 ∆Yt = α∆Xt – ѳ(Yt-1 – γXt-1)+µ t (11) Where µ t is the disturbance term with zero mean, constant variance and zero covariance. Parameter α takes into account the short run effect on Y of the changes in X, while γ measures the long-run equilibrium relationship between Y and X that is: Yt = γXt + µ t (12) Where Yt-1 – γYt-1 + µ t-1 measures the divergence (errors) from long-run equilibrium. Also ѳ measures the extent of error correction by adjustment in Y and its negative sign indicates that the adjustment is in the direction which restores the long-run relationship (Hallam and Zanoli, 1993). In order to estimate equation (5), Engle and Granger (1987) proposed a two stage process. Firstly the static long run cointegration regression (6) is estimated to test cointegration between the two variables. If cointegration exists the lagged residuals from equation (5) are used as error correction term in the Error Correction Model (in equation 6) to estimate the short run equilibrium relationship between the variables in the second stage. The validity of the Error Correction Models (ECMs) depends upon the existence of a long-run or equilibrium relationship among the variables (Mohammed, 2005). The Error Correction Model (ECM) has several advantages. It contains a well-behaved error term and avoids the problem of autocorrelation. It allows consistent estimation of the parameters by incorporating both short-run and long-run effects. Most importantly all terms in the ECM are stationary. It ensures that no information on the levels of the variables is lost or ignored by the inclusion of the disequilibrium terms (Mohammed, 2005). ECM solves the problems of spurious correlation because ECMs are formulated in terms of first difference which eliminates trends from the variables (Ganger and Newbold, 1974). It avoids the unrealistic assumption of fixed supply based on stationary expectations in the partial adjustment model. 2.6 Supply Response Models This study estimated the total area cultivated and aggregate output of rice in Ghana using double logarithmic regression models. Area cultivated of rice (lgarea) is a function of own price (Lrp), rainfall (lgrain), aggregate output (lgoutput) and price of maize (lmp). The equation used for the regression is: Lgarea = f1 (Lgoutput, Lrp, Lgrain, Lmp) (13) Aggregate output of rice (lgoutput) is a function of the area cultivated (lgarea), the price of rice (Lrp), price of maize (Lmp), rainfall (lgrain). The estimating equation is: Lgoutput = f2 (lgrain, Lrp, Lmp, Lgarea)1. (14) 3. Empirical Application and Results 3.1 Results for linear regression Table 1: The results of the regression analysis for the trend of the variables against time Variable Coefficient Std error t-statistic R2 F-statistic Prob Lgarea 1.334274 1.149167 1.61079 0.035154 0.253046 0.2530 Lgoutput -13.86625 68.09380 -0.20364 0.005889 0.844432 0.8444 lgyield -25.99348 35.51112 -0.73198 0.071100 0.487957 0.4880 Lrp 2.588941 5.221601 4.956136 0.399186 24.58311 0.0000 As can be seen from table 1 the results indicated that the regression for area, output and yield turned out insignificant. Only the trend of real price of rice was significant at 1% significance level. Real rice price yielded a positive coefficient of 2.589 which implies that for each year the real price of rice grew by 2.589 units. 3.2 Unit Root Test Results As a requirement for cointegration analysis the data was tested for series stationarity and to determine the order of integration of the individual variables. For cointegration analysis to be valid all series must be integrated of the same order usually of order one (Towsend, 2001). Eviews was used to perform these tests. 1 Price of maize is included because we assume that the same resources (land type, fertilizer etc.) can be used to produce both maize and rice. Hence, a rise in the price of maize will pull resources away from rice production to maize production. 6
  • 7. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 The data series on annual acreage cultivated (Lgarea), aggregate output (Lgoutput), aggregate yield (Lgyield), real price of rice (Lrp), real price of maize (Lmp), rainfall (Lgrain) was tested for unit root for the study period 1970 - 2008. The Augmented Dickey-Fuller test was used for this test. The results are presented below. Table 2: Results of unit root test at levels Series ADF test Mackinnon Lag-length Prob Conclusion statistic critical value Lgarea 2.221125 3.615588 2 0.2024 Non-stationary Lgoutput 1.519278 5.119808 2 0.4582 Non-stationary Lgyield 0.103318 4.580648 2 0.9419 Non-stationary Lmp 0.378290 3.621023 2 0.9026 Non-stationary Lrp 1.429037 3.615588 2 0.5579 Non-stationary Lgrain 3.006033 2.963972 7 0.0457 Stationary The results of the unit root test after first differencing are presented in table 3. Table 3: Results of unit root test at first differences Series ADF test Mackinnon Lag-length Prob Conclusion statistic critical value Lgarea 7.517047 3.621023 2 0.0000 I(1) Lgoutput 9.577098 3.621023 2 0.0000 I(1) Lgyield 7.517047 3.621023 2 0.0000 I(1) Lmp 10.02189 3.621023 2 0.0000 I(1) Lrp 6.346392 3.626784 2 0.0000 I(1) Note; All variables are in log form. The ADF method test the hypothesis that H0 : X ~ I(1), that is, has unit root (non-stationary) against H1 : X ~ I(0), that is, no unit root (stationary). The Mackinnon critical values for the rejection of the null hypothesis of unit root are all significant at 1%. Lgarea denotes log of area cultivated, Lgoutput denotes log total output, Lgyield denotes log of yield, Lmp denotes log of maize price, Lrp denotes log of rice price and Lgrain denotes log of rainfall. The results of the unit root tests showed that all the series are non-stationary at levels except for rainfall which is stationary at levels as shown in table 2 above. However as expected all the non-stationary series became stationary after first differencing. From table 2 the null hypothesis of unit root could not be rejected at levels since none except rainfall of the ADF test statistics was greater than the relevant Mackinnon Critical values. Hence the null of the presence of unit root is accepted. However the hypothesis of unit root in all series was rejected at 1% level of significance for all series after first difference since the ADF test statistics are greater than the respective Mackinnon critical values as shown in table 3 above. 3.3 Cointegration Results When the order of integration of the data series have been established, the next step in the process of analysis is to determine the existence or otherwise of cointegration in the series. This is to establish the existence of valid long-run relationships between variables. Basically there are two most commonly used methods to test for cointegration. These were suggested by Engle and Granger (1987) and Johansen and Juselius (1990). This study applies the Johansen approach which provides likelihood ratio tests for the presence of number of co-integrating vectors among the series and produces long-run elasticities. The Error correction model was then used to estimate short-run elasticities. 7
  • 8. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 3.3.1 Supply Response of Rice Output Firstly, the Vector Error Correction Modeling (VECM) procedure using the Johansen method involves defining an unrestricted Vector Autoregression (VAR) using the following equation. Zt=AtZt-1+…+AkZt-1+µ t (15) Likelihood Ratio (LR) tests were conducted with maximum of three lags due to the short time series. The results are shown in table 4. Table 4: Results for lag selection output model VAR Lag Order Selection Criteria Endogenous variables: OUTPUT AREA MP RP Exogenous variables: C RAIN Lag LogL LR FPE AIC SC HQ 0 -1208.968 NA 6.64e+32 86.92626 87.30689* 87.04262 1 -1184.928 37.77692 3.84e+32 86.35198 87.49387 86.70107 2 -1174.563 13.32579 6.37e+32 86.75452 88.65766 87.33633 3 -1138.068 36.49514* 1.92e+32* 85.29058* 87.95499 86.10511* * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion LogL: Log-likelihood The results indicate that the Akaike Information Criterion (AIC) and the Hannan-Quin information criterion (HQ) selected lag order three while the Scharz Bayesian Criterion (SBC) selected the lag order zero. Thus, this study selects the lag order three as the order for the VAR models. The next step in the Johansen method is to test for the number of co-integrating vectors among the series in the model. The results for the cointegration test imply that both the trace test and the maximum eigen value test selects the presence of one co-integrating vector, thus it can be concluded that the variables in the model are co-integrated. The Johansen model is a form of Error Correction Model. When only one co-integrating vector is established its parameters can be interpreted as estimates of long run co-integrating relationship between the variables (Hallam and Zanoli, 1993). This implies that the estimated parameter values from this equation when normalized on output are the long run elasticities for the model. Eviews automatically produces the normalized estimates. These coefficients represent estimates of long-run elasticities with respect to area cultivated, real price of rice and real price of maize. The normalized cointegration equation for rice output is given below; Output=0.216748area+0.241522rp-0.009975mp-12841.69 (16) Since cointegration has been established among the variables, then the dynamic ECM can be used for supply response analysis since it provides information about the speed of adjustment to long run equilibrium and avoids the spurious regression problem between the variables (Engle and Granger, 1987). The ECM for rice output is presented as; ∆output = α0+ 1i∆outputt-i + 2i∆areat-i + 3i∆rpt-I + 4i∆mpt-I + 5rain – θECt-I (17) Where θECt-I = α (β1outputt-I – β2areat-I – β3rpt-I – β4mpt-I) 8
  • 9. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 In the ECM model above, the α’s explain the short run effect of changes in the explanatory variables on the dependent variable whereas β’s represent the long run equilibrium effect. θECt-I is the error correction term and correspond to the residuals of the long run cointegration relationship, that is the normalized equation (16). The negative sign on the error correction term indicates that adjustments are made towards restoring long run equilibrium. This representation ensures that short run adjustments are guided by and consistent with the long run equilibrium relationship. This method provides estimates for the short run elasticities, that is the coefficients of the difference terms and, whereas the parameters from the Johansen cointegration regression are estimates of the long run elasticities (Townsend and Thirtle, 1994). In selecting the best ECM estimates, models with lag lengths are estimated and those with insignificant parameters are eliminated. Note that the estimates presented above represent the short run effect of the explanatory variables on the dependent variable. The long run effect is captured by the estimates of the normalised Johansen regression results presented in equation (16). The diagnostic tests are the t-ratio test of the coefficients, LM test for autocorrelation and the Jarque Berra test for normality of residuals. Table 5: ECM results for rice output Variable Coefficient t-statistic Prob ∆lgarea 0.017611 0.668907 0.0510 ∆lgoutput(-1) 0.523393 2.283959 0.0319 lgRain 0.003740 0.997918 0.0328 ∆lgmp -0.001107 -0.298174 0.7683 ∆lgrp 0.009922 0.307758 0.0761 Residual -0.174253 -1.321005 0.0043 R2 (0.754242) F-statistic (2.523411) Prob(F-stat) 0.058261 It indicates that aggregate rice output is dependent on area cultivated, previous year’s output, and previous year’s price of rice. The coefficient of maize price was not significant. An R2 of 0.754 indicates that 75% of the variation in the dependent variable can be accounted for by variation in the explanatory variables. The results indicate that area cultivated was significant at 10%, with elasticity of 0.0176, which implies that a one percent increase in area cultivated of rice will lead to a 0.018 % increase in output in the short run. This is rather on the low side. This can be attributed to the fact that an increase in the land cultivated without a necessary increase in the resources of the farmer will increase adverse effect on the minimal resources available to the farmer hence resulting in this marginal increase in output. This result also indicates that the productivity of the farmer reduces with increase in farm size. The long run elasticity of area cultivated is 0.2167 (in equation (16)) is higher than the 0.0176 for the short run. This indicates that over the long run farmers adjust their farm sizes more to output than in the short run. That is, a 1% increase in area cultivated will lead to 0.217% increase in output in the long run. Lagged output has elasticity of 0.523393 in the short run and is significant at the 5% significance level implying that a one percent increase in output in a year will lead to a 0.52% increase in output the subsequent year. This simply means that an increase in output will make capital available for the farmer to invest in the subsequent year’s rice production activities. This is only true if the additional resource is invested into the following year’s rice production activities. Rainfall is significant at 5%. An elasticity of 0.003740 for rainfall indicates that a 1% increase in rainfall will result in a 0.004% rise in output. This low impact of rainfall can be explained by the fact that a large proportion of total rice output is produced under the irrigation projects across the country. This makes the response of output to rainfall highly inelastic. A coefficient of 0.009922 which is significant at 10% for real price of rice indicates that a 1% rise in real 9
  • 10. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 prices will lead to 0.01% increase in output the subsequent year in the short run. This makes prices also inelastic. This low rice price elasticity suggests that farmers do not always necessarily benefit from increasing prices due to the structure of the marketing system. Middlemen and other marketing channel members purchase the rice from farmers at the farm gate and thereafter transport it to market centres to be sold. Hence when there is a rise in prices a little fraction of it is transmitted to the famer. The low price elasticity could also be attributed to the fact that farmers are hindered by an array of constraints such as land tenure issues, rainfall variability, lack of capital resources and credit facilities which limits their capacity to respond to price incentives. The long run coefficient of 0.2415 indicates that 1% percent increase in real prices will lead to a 0.24% increase in output. The long run elasticity far exceeds the short run. This is because over the long run when prices show a continual rise, farmers are able to accumulate capital enough to enhance production. The residual which is the error correction term is significant at 1% and has the expected negative sign. It measures the adjustment to equilibrium. Its coefficient of -0.174 indicates that the 17.4% deviation of rice output from long run equilibrium is corrected for in the current period. This slow adjustment can be attributed to the fact that famers in the short run are constrained by technical factors as mentioned earlier which limits their ability to adjust immediately to price incentives. In the long run maize price is significant at 1%. With a negative coefficient of 0.009975 it implies that a 1% increase in the price of maize will lead to 0.01% reduction in the output of rice. This is to say that resources will be diverted to maize production relative to rice production leading to the fall in rice output. However, Maize price was not significant in the short run. The results of the LM test of serial correlation for up to fifth order show that there is no serial correlation in the data set. 3.3.2 Supply Response of Area Cultivated Testing for the selection of lag length yielded the same results for both the acreage and output models. Hence the same lag order three is therefore used for the Vector Error Correction model (VEC). The trace test selects one co-integrating vector while the eigen value test selects two co-integrating vectors. But since the trace test is the more powerful test (Mohammed, 2005), the result from the trace test is used here. Thus the acreage model has one co-integrating vector. The normalised cointegration equation for area cultivated is given as; lgarea =4.613649lgoutput+3.11429lrp–0.46020lmp–59247 (18) The ECM for area cultivated is presented as; ∆lgarea = α0 + 1i ∆lgoutputt-i + 2i ∆lgareat-i + 3i ∆lgrpt-I + 4i ∆lgmpt-I + 5l lgrain – θECt-I (19) Where θECt-I = α (β1areat-I – β2outputt-I – β3rpt-I – β4mpt-I) The result of the estimated ECM is given in table 6 below. Table 6: ECM results for area cultivated Variable Coefficient t-statistic Prob ∆lgarea(-1) 0.169747 1.343021 0.1192 ∆lgoutput(-1) 12.80810 1.816372 0.0824 lgRain 0.003984 0.138622 0.0891 ∆lmp(-1) -0.011350 -0.401819 0.0691 ∆lrp(-1) 2.016747 1.440683 0.0265 Residual -0.435411 -1.043641 0.0047 R2 (0.770669) F-statistic Prob(F-stat) 0.017301 (1.707148) An R2 of 0.77067 indicates that 77% 0f the variation in the dependent variable is accounted for by variation in the explanatory variables. Output is significant at 10% with an elasticity of 12.80 indicating that a 1% increase in output this year will increase land cultivated in the subsequent year by 12.8% in the short run. Thus acreage cultivated is highly elastic with respect to output. An increase output level gives the farmers an opportunity to acquire necessary resources and equipment to put more land under cultivation. Output 10
  • 11. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 elasticity is even higher than that for real prices. This can be attributed to the fact that a large proportion of output is for household consumption and thus is independent of prices. Increased output alone is enough motivation to increase acreage under cultivation. In the long run output is insignificant. This because in the long run land will get exhausted and output will be dependent on productivity (and not the area of land cultivated). Rainfall is significant at 10% with a coefficient of 0.003984; this implies a 1% increase in rainfall leads to 0.004% increase in area cultivated. This low response to rain can be attributed to the fact that large areas under rice cultivation are in irrigated fields which depends less on rainfall for cultivation. Rainfall is an exogenous variable (I(0)), hence it measures short run effect. Maize price is significant at 10% with a negative coefficient of -0.011350 in the short run and -0.460 in the long run. This means over the long run if maize prices are continually increasing farmers will commit more resources into maize farming relative to rice farming. Thus as the price of maize rises area under rice cultivation reduces both in the short and long run. In the short run a 1% increase in maize price reduces area cultivated of rice by 0.0114% and by 0.46% in the long run. Real price of rice was significant at 5% with elasticities of 2.017 and 3.11 in the short run and long run respectively, thus price is elastic. This implies a 1% percent increase in real price of rice will result 2.017% and 3.11% increase in acreage cultivated in the short run and long run respectively. This is expected since in the long run farmers are able to adjust to overcome some the major challenges of production and hence are able to adjust more to price incentives. The error correction term has the expected negative sign and with a coefficient of 0.4354 indicating that 43.54% of the deviation from long run equilibrium is corrected for in the current period. The results of the test for serial correlation show that is no autocorrelation in the series. The Jarque-Berra test statistic of 10.18 with probability value of 0.25 implies that the residuals are normally distributed. 4. Conclusions Economic theory in the past had been based on the assumption that time series data is stationary and hence standard statistical techniques (Ordinary Least Squares regression (OLS)) designed for stationary series was used. However, it is now known that many time series data are non-stationary and therefore using traditional OLS methods will lead to invalid or spurious results. To address this problem, the method of differencing was introduced. Differencing according to Granger however leads to loss of valuable long-run information. Granger and Newbold (1974) introduced the technique of cointegration which takes into account long-run information therefore avoiding spurious results while maintaining long-run information. This study presents an analysis of the responsiveness of rice production in Ghana over the period 1970-2008. Annual time series data of aggregate output, total land area cultivated, yield, real prices of rice and maize, and rainfall were used for the analysis. The Augmented-Dickey Fuller test was used to test the stationarity of the individual series. The Johansen maximum likelihood criterion is used to estimate the short-run and long-run elasticities. The trend analysis for rice output, rice price, acreage cultivated, and yield revealed that only rice price is significant at 1% significance level. The results imply that for each year, the price of rice will increase by 2.589 units. All the time series data that was used were tested for unit root. They were found to be non-stationary at levels but stationary after first differencing at the one percent significance level except for rainfall which was stationary at levels at the 5% significance level. The Likelihood Ratio tests selection of lag order selected order three by the AIC and HQ criteria for both models. The Johansen cointegration test selected one cointegration vector (in both rice output and rice price models) indicating that the variables are co-integrated. The diagnostic tests of serial correlation and normality test was done using the Lagrange Multiplier test for autocorrelation and the Jarque-Berra test for normality of residuals. The results indicate no serial correlation and normally distributed residuals. The land area cultivated of rice was significantly dependent on output, rainfall, real price of maize and real price of rice. The elasticity of lagged output was 12.8 in the short run and was significant at 1%. However, this elasticity was not significant in the long run. Rainfall had an elasticity of 0.004 and significant at 10%. Also real price of maize had negative coefficient of -0.011 which was significant at 10%. This is consistent with theory since a rise in maize price will pull 11
  • 12. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 resources away from rice production into maize production. The coefficient of the real price of maize estimated by Chinere (2009) was -0.066 for rice farming in Nigeria. This is higher than the -0.011 estimated for Ghana (in this study). The real price of rice had an elasticity of 2.01 and significant at 5% in the short run and an elasticity of 3.11 in the long run. The error correction term had the expected negative coefficient of -0.434 which is significant at 1%. It was found that in the long run only real prices of maize and rice were significant with elasticities of -0.46 and 3.11 respectively. The error correction estimated by Chinere (2009) was -0.575. This indicates that adjustment to long run equilibrium is faster in Nigeria. This could be attributed to better agricultural infrastructure in Nigeria compared to Ghana. The error correction for Indian rice in the rice zone as estimated by Mohammed (2005) was -0.415. This could be attributed to the fact that Indian agriculture was highly constrained by land problems hence leaving room for little adjustments in terms of increasing acreage cultivated. The aggregate output of rice in the short run was found to be dependent on the acreage cultivated, the real prices of rice, rainfall and previous output with elasticities of 0.018, 0.01, 0.004 and 0.52 respectively. Real price of rice and area cultivated are significant 10% level of significance while rainfall and lagged output are significant 5%. In the long run aggregate output was found to be dependent on acreage cultivated and the real price of rice and real price maize with elasticities of 0.218, 0.242 and -0.01 respectively at the 1% significance level. Mythili (2008) used panel data to estimate the supply response of Indian farmers. His findings also support the view that farmers’ response to price is low in the short-run and their adjustment to reaching desired levels is low for food grains. The analysis showed that short-run response in rice production is lower than long-run response as indicated by the higher long-run elasticities. This is because in the short run the farmers are constrained by the lack of resources needed to respond appropriately to incentives. In the short-run inputs such as land, labour, and capital are fixed. To address these concerns government should devise policies to make land available to farmers so that prospective farmers could increase acreage cultivated. More irrigation facilities should be constructed to put more land under cultivation. It was also observed that the acreage model had higher elasticities than the output model. Thus farmers tend to increase acreage cultivated in response to incentives. This implies that farmers have more control over land than the other factors that influence output. Efforts should be put in place to make the acquisition of inputs such as tractors and fertilizer more accessible and affordable to farmers, and to improve the road network linking farming communities and the urban centres. Price control policy should be introduced and enforced to address the problem of frequent price fluctuation which is the main reason for the low response to prices. Since farmers are aware of these price fluctuations they are reluctant to immediately respond positively to price rises. Though there is a market for rice in Ghana, recent developments have shown that consumers prefer foreign polished rice; therefore, government should put in place the needed infrastructure to process the locally produced rice to ensure the sustainability of local rice production. References Box G. E. P. and Jenkins G. M. (1976). ‘Time Series Analysis: Forecasting and Control’, 2nd ed, San Francisco, Holden-day. Cambridge University Press. Chinyere G. O. (2009). ‘Rice output supply response to changes in real prices in Nigeria; An Autoregressive Distributed Lag Model approach’. Journal of Sustainable Development in Africa, 11(4), 83 -100. Clarion University of Pennsylvania, Clarion, Pennsylvania. Davidson, J. E. H., Handry, D. F., Srba F., and Yeo S. (1978). ‘Econometric modeling of aggregate time series relationships between consumer’s expenditures and income in the UK’, Economic Journal. 88, 661-692. 12
  • 13. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Dickey D. A. and Fuller W. A, (1981). ‘Likelihood ratio statistics for autoregressive time series with a unit root’. Econometrica, 49, 1057-1072. Engle R. F, and Granger C. W. J. (1987). ‘Cointegration and error correction: Representation, estimation and testing’, Econometrica, 55, 251 -276. FAO (1996). ‘Report of expert consultation on the technological evolution and impact for sustainable rice production in Asia and Pacific’. FAO-RAP Publication No. 1997/23, 206 pp. Regional Office for the Asia and the Pacific. Granger C. W. J. (1981). ‘Some properties of time series data and their use in econometric model specification.’ Journal of Econometrics, 16 (1), 121-130. Granger C. W. J. and Newbold P. (1974). ‘Spurious Regression in Econometrics’, Journal of Econometrics 2, 111-120. Ghana Statistical Service (2011). ‘Ghana Statistical Service release’. Hall S. G. (1991). ‘The effect of varying length VAR on the maximum likelihood estimates of co-integrating vectors.’ Scottish Journal of Political Economy, 38, 317- 323. Hallam D. and Zanoli R. (1993). ‘Error Correction Models and Agricultural Supply Response’. European Review of Agricultural Economics, 20, 151-166. Hansen S. and Juselius K. (1995). ‘Cats in rats: Cointegration analysis of time series, Evanston, Illinois. Harris R, (1995). Using cointgration analysis in econometric modeling. Prentice Hall, Harvester Wheatsheaf, England. Johansen S. and Juselius K. (1990). ‘Maximum likelihood estimation and inference on cointegration- with application to the demand for money’. Oxford bulletin of Economics and statistics, 52, 170-209. Kherallah M C., Delgado E., Gabri-Madhin N. M., and Johnson M., (2000). ‘The Road Half Travelled: Agricultural Market Reform in Sub-Saharan Africa’. International Food Policy Research Institute, Food Policy Report 10. Washington D.C. Kiel Working Paper No. 1016. Krueger A. O., Schiff M, and Valdés A. (1992). ‘The Political Economy of Agricultural Pricing Policies’. A World Bank Comparative Study, Baltimore. Lewis W. A, (1954). ‘Economic Development with Unlimited Supplies of Labour’. Manchester School 22: 139–191. Ministry of Food and Agriculture, Ghana (2005), Ministry of Food and Agriculture report. Mohammed S. (2005). ‘Supply response analysis of major crops in different agro-ecological zones in Punjab.’ Pakistan Journal of Agricultural Research, 2007, 124 (54). Mythili G. (2008). ‘Acreage and yield response for major crops in the pre – and post reform periods in India: A dynamic panel data approach’, Report prepared for IGIDR-ERS/USDA project: Agricultural markets and policy, Indira Gandhi Institute of Development Research, Mumbai. 13
  • 14. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Nerlove M. and Bachman K L., (1960). ‘Analysis of the changes in agricultural supply, Problems and Approaches’. Journal of farm economics. 3(XLII), 531-554. National Rice Policy Development (2009). National Rice Policy Development Report. Statistical Research and Information Division (SRID). (2005). Statistics Research and Information Department Release. Thiele R., (2000). ‘Estimating the aggregate supply response, a survey of techniques and results for developing countries.’ Kiel Institute of world Economics Duuesternbrooker Weg 120 241015 Kiel, Germany. Kiel working paper No 1016 Rainer Thiele. Thiele R, and Wiebelt M, (2000). ‘Adjustment lending in Sub Saharan Africa Kiel Institute of World Economics.’ Kiel Discussion Papers 357, Kiel. Tobacco In Zimbabwe Contributed Paper Presented at the 41st Annual. Townsend R and Thirtle C. (1994). ‘Dynamic acreage response. An error correction model for maize and tobacco in Zimbabwe’. Occasional paper number 7 of the International Association Agricultural Economics. Townsend T. P. (2001). "World Cotton Market Conditions". Beltwide Cotton Conferences, Proceedings, Cotton Economics and Marketing Conference, National Cotton Council, Memphis, TN, pp. 401-405. World Bank. (1997). "Cotton and Textile Industries: Reforming to Compete." Vol. I, II, Rural Development Sector Unit; South Asian Region, World Bank. Report No. 16347- IN, Washington, D.C, 1997. 14
  • 15. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Sustainable Use of Water Resources in the Form of Pisciculture to Generate Income in West Bengal -A study report. Bairagya Ramsundar Department of Economics, SambhuNath College, Labpur, Birbhum, West Bengal, India, Pin-731303 Mobile No. +919474308362 Fax: +913463 266255, E-mail: ramsundarbairagya@gmail.com Received: October 1st, 2011 Accepted: October 11th, 2011 Published: October 30th, 2011 Abstract Though the two-third of earth’s volume is comprised of water the world is facing the problem of scarcity of fresh- water. Because most of the water is sea water which is salt in nature. It is the modern day tragedy that due to the scarcity of these valuable lives saving natural resources life will exist no more. So the sustainable use of this resource is very much important today. India was blessed with vast inland natural water resources. But Indian Economy faces the problem of proper utilization of these huge water resources spread over its vast stretches of land. A proper policy for utilization of these resources would become a governing direction of economic growth. The role of fisheries in the country’s economic development is amply evident. It generates employment, reduces poverty, generates income, increases food supply and maintains ecological balance between flora and fauna. Scientists have shown that 3 bighas forest areas are equivalent to 1 bigha plank origin in water bodies which create more O2. This paper intends to develop a scientific plan use of water with a view to sustainable management of water resources to generate income from fishery in the field of pisciculture by using the scarce water resources in a sustainable eco-friendly manner. Keywords: Ecology, Growth, Perishable, Pisciculture, Pollution, Project, Sustainable development, Composite fish culture 1. Introduction According to 2001 Census India is the second (next to China) largest populous country in the world about 17.5% of world populations live in India which covers only 2.4% (Dutt R. and Sundharam K.P.M. 2009) of geographical land area. The density of population is 324 persons per sq. km. If the current growth rate of population (i.e. 2.11% per annum) continues within 2030 India will be the most populous country in the world creating a food scarcity and a huge amount of unemployment in a massive scale. About 68% of total population spends their livelihood from agriculture and the per capita income is very low. Water scarcity is too much related to food scarcity. Hence it is very crucial to develop a scientific plan of water use with a view to sustainable management of water resource. Management of shortage of water and management of water pollution are complex task. These issues have drawn the attention of the developed and developing countries as well as the various national and international organizations including the Johannesburg Summit 2002, Year 2003 has been declared as year of Fresh -Water. But in India there are huge prospect to utilize the unused fresh water resources for pisciculture which can play an important role in this aspect. Realizing its importance during the 5th - five year plan the Government of India introduced 15
  • 16. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 beneficiary-oriented programme in the form of a pilot project entitled ‘Fish Farmers Development Agency’ (FFDA) to provide self employment, financial, technical and extension support to fish farming in rural areas. In 1974-75 this Programme was further extended under World Bank-assisted, Inland fisheries project to cover about 200 districts of various states in India. 2. Sustainable development and Pisciculture Sustainable development is a pattern of resource use that aims to meet human needs while preserving the environment so that these needs can be met not only in the present, but also for future generations. The term was used by the Brundtland Commission which coined what has become the most often-quoted definition of sustainable development as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs”. It is usually noted that this requires the reconciliation of environmental, social and economic demands - the “three pillars” of sustainability. This view has been expressed as an illustration using three overlapping ellipses indicating that the three pillars of sustainability are not mutually exclusive and can be mutually reinforcing. Sustainable development ties together concern for the carrying capacity of natural systems with the social challenges facing humanity. As early as the 1970s “sustainability” was employed to describe an economy in equilibrium with basic ecological support systems [Wikipedia]. A primary goal of sustainable development is to achieve a reasonable and equitable distributed level of economic well being that can be perpetuated continually for next generation. Thus the field of sustainable development can be broken into three constituent parts i.e. environmental, economic and social sustainability. It is proved that socio- economic sustainability is depended on environmental sustainability because the socio- economic aspects, like agriculture, transport, settlement, and other demographic factors are born and raised up in the environmental system. All the environmental set up is depended on a piece of land where it exists. Water is a renewable natural resource and is a free gift of nature. In the early days the supply of water was plenty in relation to its demand and the price of water was very low or even zero but in course of time the scenario has totally changed (Bairagya R. and Bairagya H. 2011). Water is a prime need for human survival and also an essential input for the development of the nation. For sustainable development management of water resources is very important today. Due to rapid growth of population, expansion of industries, rapid urbanization etc. the demand of water rises many-fold which is unbearable in relation to its resources in the earth. All the environmental set up is depended on a piece of land where it exists. So, to get sustainable environmental management, sustainable land management is necessary. As a result the use of water resources in the form pisciculture in a sustainable manner may gain priority. 4. Importance of the study Social scientists have always identified the rural areas for investigation. In the case of India to a large number of studies have been carried out in rural situations including panchayats and co-operative societies. Though many research works have been done in the biological and marine sciences, the economic investigations of pisciculture have not yet been done so far. In this respect the present study has a clear economic importance for the upliftment of the rural economy at the grass root level. Thus pisciculture has a positive effect on ecology and hence to maintain ecological balance a meaningful use of unused water resources has an important role. Scarcity of water is a modern day tragedy human society will exist no more 16
  • 17. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 due to these scarce resources. So a meaningful use of this resources in the form of pisciculture to generate income of the rural poor gain topmost priority. The state of West Bengal plays an important role for the implementation of the programme. Though the two districts Burdwan and Birbhum of W.B. are primarily agricultural districts, there is huge scope for pisciculture. A few studies have been undertaken by several experts, notably, D. Prasad (1968), R. Charan (1981), A.V. Natarajan (1985), K. M. B. Rahim (1992,93), A Chakravorty (1996), I Guha and R. Neogy (1996), P.K. Ghosh (1998) and others on the economic evaluation of pisciculture. Though these are useful guides to researchers, yet there is ample scope for further works relating to pisciculture in the rural areas of W.B. Besides, there is the necessity of developing studies concerning the impact of these programmes on the rural economy. The present study is a modest attempt in remedying this inadequacy. 4.1 Objectives This pilot study was conducted on the following objectives i) To generate employment in rural India. ii) To rise in food supply and reduce mal-nutrition. iii) For proper utilisation of unused water resources. iv) To maintain ecological balance in a sustainable manner. V) Identification of fish farmers suitable and willing to develop fish farming in ponds.vi) Arranging training in organized manner and ensuring extension services to fish farmers. 4.2 Methodology of the study 4.2.1 Selection of study area The present study was confined to survey the rural areas of Burdwan and Birbhum districts of W. B. in respect of implementation of the programme of Fish Farmers Development Agency (Mishra S. 1987). In the selection of districts following considerations weighed most: i) Both the districts are covered with water areas constituting half of the total inland water resources. ii) There is a heavy concentration of tanks and ponds in both the districts. iii) Both the districts are considered as having one homogeneous agro-climatic zone in view of the broad similarities of soils, climate and other features. Since they are also neighboring districts a suitable comparison can easily be made. iv) In both the districts, the FFDA programmes are being implemented in full fledged form by the Government authority. v) Data from both the districts can be obtained because of my personal knowledge about the two districts. 4.2.2 Selection of sample Keeping in view the time factor, limited fund and limited ability it was not possible to collect data from all the recorded fish farmers of the two districts. At first 5 blocks from each district have been purposefully selected. Then 10 recorded fish farmers from each block have been selected purposefully. Thus 50 fish 17
  • 18. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 farmers from each district have been selected for interview. For comparative analysis of data each farmer was taken as the unit. For this study data have been collected on different aspects of the programme such as farmers’ income, water area, finance, total production, product price, cost of production, profit, duration of training period etc. The data have been collected by personal interview method through a questionnaire. Thus the collected data are entirely primary in nature. 4.2.3 Location map of the study area The state of West Bengal lies between latitude 21038/ to 27010/ North and longitude 85038/ to 89050/ East. The district Burdwan lies between 22056/ and 25053/ North latitude and 86048/ and 88025/ East longitude. The district Birbhum lies between 23032/30// and 24035/00// North Latitudes and 88001/40// and 87005/25// East Longitudes shown in figure-1. 5. Fisheries in India Indian fisheries can broadly be divided into two categories: i) Inland fisheries & ii) Marine fisheries. Further Inland fisheries can be classified into two types: i) Capture & ii) Culture. Capture fisheries consist of rivers, lakes canals etc. where farmers do not cultivate fishes. Natural breeding process is the common phenomenon there. On the other hand, culture fisheries consist of ponds, tanks, swamps, marshes etc. In this case, farmers have to sow fish seeds, nurse it and send it to proper size before harvesting. India is the third largest producer of fish in the world and the second in inland fish production. Indian fishing resources comprising of 2 million sq.km.of EEZ for deep sea fishing, 7,520 km. of coast line, 29,000 km. of Rivers, 1.7 million Ha. of reservoirs, 1 million Ha. of brackish water and .8 million Ha. of ponds, lakes and tanks for inland and marine fish production (Giriappa S. (ed) 1994) . About 14 million fishermen draw their livelihood from fishery. During the period 1981 to 2002 the contribution of fishery to GDP has increased from Rs.1230 crores to Rs.32060 crores. The fish production increased from 0.7 million tons in1951 to 6.8 million tons in 2006. 5.1 Fisheries in West Bengal West Bengal has a vast water resource potentiality. By utilizing these water resources there is a huge prospect of pisciculture (Chakraborty S. K.1991). These resources can be divided into two categories: i) Inland water resources and ii) Marine water resources. Inland resources constitute ponds, rivers, marshy lands, canals, reservoirs etc. Water Resources of West Bengal shown in figure-2. It should be noted that tanks/ponds occupy the major share i.e. 46.70% of total inland water resources. But out of 2, 76,202 Ha. area under ponds and tanks only 2, 20,000 Ha. i.e. 79.65% are presently used for pisciculture which means 20.35% remains unused. Moreover, out of 5, 91,476.71 Ha. total inland water resource only 2.87000 Ha. water area is brought under pisciculture i.e. 48.56% are presently used and 51.44% remains unused. These unused water resources can be brought under pisciculture through proper utilization. 18
  • 19. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 In West Bengal marine fishery has a substantial share amounting to a coast line of 158 km. inshore area up to 10 fathoms depth is 770 sq. km. (Mamoria C.B. 1979), offshore area (10-40) fathoms depth is 1813 sq. km. and a continental shelf up to 100 Fathom is 17,049 sq.km. Out of 19 districts of West Bengal only two districts East- Midnapur and South 24-Parganas are coastal. West Bengal is on the top of the list in fish production in the country. With the passage of time, more and more people are getting themselves involved in fishery. As fish constitutes the staple food of the people efforts are being made to augment fish production. From the period 1986 to 2000, the total fish production increased from 424000 tons to 1045,000 tons (i.e. 2.46 times). At the same period the inland fish production increased 2.25 times and marine fish production increased 4.50 times. In the year 1986, the share of inland and marine fisheries to total fish production were 91% and 9% respectively. But in the year 2000, the share of inland and marine fisheries to total fish production are 83% and 17% respectively. Thus we see that during the period 1986 to 2000, the share of inland fish to total production declined (from 91% in 1986 to 83% in 2000) and the share of marine fisheries increased (from 9% in 1986 to 17% in 2000). Not only in fish production but also in the demand for fish West Bengal is the highest in the country. The domestic demand for fish in West Bengal is high because almost all the people of West Bengal are fish-eating. But the state has a higher demand for fish than its production of fish i.e. this state has a deficit in fish supply. To meet this gap the state West Bengal has to import fish from other states like Andhra Pradesh, Tamil Nadu etc. At the same time various efforts have been made to augment fish production to bridge this gap. 5.2 Fisheries in Burdwan district The district of Burdwan is mainly an agricultural district though it is also well advanced in industrial production. It is called the ‘granary’ of West Bengal. The district is filled with well fertile productive land. Not only that, the district is also well endowed with a large number of water bodies in the form of rivers, ditches, canals, marshy lands, ponds, tanks etc. The principal rivers are Ajoy, Damodar and Kunur etc. There is a good prospect of pisciculture by utilizing these water resources. The distribution of total water resources in Burdwan district (i.e. 66480.82 Ha.) is shown in figure-3. Though the district has huge water resources, during the period 1982-1998 only 50% of this water area was brought under scientific pisciculture. In the year 1999, the district’s total fish production was 55,500 metric tons while demand in that year was 65,000 metric tons. Thus there was a deficit of 9,500 metric tons. To meet this demand the district had to import fish from neighboring states. The district will be self-sufficient in fish production if the unused 50% water resources are brought under pisciculture. 5.3 Fisheries in Birbhum district 19
  • 20. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 The district of Birbhum is a part of Rarh area. The district is well-drained by a number of rivers and rivulets. The principal rivers of this district are: Ajoy, Brahmani, Dwarka, Kopai, Mayurakshi etc. But most of the rivers remain dried up during a greater part of the year. Due to this adverse natural factor pisciculture has not made any significant progress in this district. Agriculture is the main occupation of the common people of this district. The main water areas for pisciculture of this district are rivers, tanks, ponds, khals, Bills, baors, reservoirs etc. The distribution of total water resources in Birbhum district (i.e. 45215.81 Ha.) is shown in figure-4. The figure-4 shows that this district has a large number of water resources. But during the period 1980-1999, only 46% of this water area was brought under pisciculture through FFDA. So there is a huge prospect of pisciculture by utilizing these unused water resources. In the year 1999 total number of fish farmers in this district was 2, 00747 out of which 56.5% were males and 43.5% were females. The total number of fisherman family in this district was 4,975. The average fish production of this district was 21,000 metric tons and the annual demand was 24,000 metric tons. Thus the district had a deficit (3000 mt) in fish production. To meet its own demand the district has also to import fish from the neighbouring states. 6. Findings 6.1 Income distribution of sample fish farmers before and after assistance from FFDA It was observed that 26 farmers in Burdwan district & 32 farmers in Birbhum district have pisciculture as the main occupation while others have pisciculture as a subsidiary occupation. Their basic occupations are agriculture, business and service and have various types of incomes from those occupations. But as income generation scheme only income from fishery was taken into account i.e. income means income earned from selling fishes. From sample survey we have collected two sets of income data (one showing income before and other showing income after the assistance of FFDA) for each fish farmer. Thus the amount of income reveals the income of the fish farmer before the receipt of assistance of FFDA and the income after the assistance of FFDA. In this regard the income comparison has done in two areas (namely within the district, between the district and the overall change of income) and‘t’ (Gujarati D.N. 2009) test was used for statistical analysis. Now the various types of incomes distribution of fish farmers of the two districts are shown in terms of four tables. From table- 1 it is clear in both the districts the average income of most of the fish farmers (i.e. 89 out of 100 fish farmers of the two districts taken together) has monthly income before assistance below Rs. 4000 i.e. they are basically come from poor family income group i) From the field survey we get that the per capita availability of fish in Burdwan district is 24 kg. per head per year while in Birbhum district it is 23.5 kg. per head per year. Now the world’s per capita availability of fish is 11.5 kg. per head per year and for developed countries it is 25 kg. per head per year. But India has a low per capita availability of 3.5 kg. per head per year only. Thus we see that the fishermen have a standard level of per capita fish consumption. The point is very important to reduce mal-nutrition (which is a common phenomenon for the country) by means of intensive pisciculture. But India has a poor per capita 20
  • 21. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 availability of fish of only 3.5 kg per head per year. Thus we see that the per capita availability of fish for these 100 farmers’ families of these two districts are much higher than that of India and world’s average and even equal to the standard of the developed countries. ii) It was found that the average monthly income of fish farmers before the assistance of FFDA in Burdwan district was Rs. 2680 and in Birbhum district it was Rs. 2045. But after the assistance of FFDA the average monthly incomes of the fish farmers of the two districts increased to Rs.3935 and Rs.3721 respectively. Thus we see that the average monthly income of fish farmers of Burdwan district has increased 1.47 times and in case of Birbhum district it has increased 1.8 times. The result indicates that the average income of fish farmers of Burdwan district was higher than the average income of those of Birbhum district in respect of both before and after the assistance of FFDA. However, the rate of increment was higher in Birbhum than that of Burdwan district. Thus we can say that the incomes of the fish farmers have improved due to pisciculture. iii) Statistical methods were adopted is to judge whether the change of income of the sample fish farmers before and after the assistance of FFDA was significant or not. This has been done in two areas: - a) Change of income within the district and b) Overall change of income of the two districts. 6.2 Change of income within the district Burdwan district- It is seen that the value of‘t’ = 3.31 is significant at .01 level, meaning thereby that the change income of sample fish farmers of Burdwan district is significant. The gain is in favor of the assistance of FFDA. It may be concluded from the result that there is a positive impact of assistance on fish farming (i.e. the production of fish). It may also be concluded that production has increased significantly in Burdwan district. Birbhum district-The result indicates that “t” is significant at .01 level. Hence it may be said that in Birbhum district the change of income of fish farmers is significant. 6.3 Overall change of income taking the two districts together It is seen from the above table that the value of‘t’ is significant at .01 level which indicates that the income of fish farmers before and after assistance differ significantly. The result reveals that the mean income after assistance is significantly greater than that before assistance. It may be concluded from the overall results considering both the districts that the impact of assistance on fish production is significant. The finding of these results was that the assistance of FFDA programme has raised the fish farmer’s income undoubtedly in both the districts. Not only that the change of income was statistically significant but also the result revealed that there was a positive impact of FFDA assistance on fish production (i.e. fish 21
  • 22. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 production has significantly increased). Moreover, it was also found that the gain of income of fish farmers of Birbhum district was higher than that of Burdwan district. Thus we can conclude that in case of the implementation of the programme of FFDA the district Burdwan has more successful achievements than the district of Birbhum. 7 Suggestions 7.1 General suggestions i) In pisciculture, fishermen are not only directly employed in fishing but also some other alternative occupations like net making, marketing of fish seed and fishery product , transport, boat making etc. many rural people earn income. Since fish is a perishable commodity proper marketing channels should be established. Hence to reduce pressure from agriculture pisciculture may be alternative occupations for generating income and employment for a large number of poor people. ii) In Burdwan district there are some open caste pits (OCP) in Ranigang coal belt and in Birbhum district also such pits are available at Khoirasole block, calamines at Md.Bazar block. Fish production may increase by utilizing these water resources for pisciculture purposes.iii) The urban waste (i.e. garbage) may be recycled as fish feed (to those ponds and water areas lying near the towns) to raise fish production and to prevent the environmental pollution in those areas. v) To make financial support for the poor fishermen Bank should grant loan on a long-term basis and at a low rate of interest in proper amount and in proper time.vi) the selection of actual beneficiary is very much essential and it should be made on the basis of need and neutrally not in politically. vii) Since fish is a perishable commodity storage facilities should be provided such that the fishermen are not forced to sell their product at a lower price. Prices of organic manures and fish seeds should be kept as low as possible or the Government should give more subsidies in the case of chemical fertilizers so that the poor fish farmers can buy them. viii) For welfare measures of the rural poor fishermen some Group and Personal Insurance Schemes, Old- Age Pension Schemes should be taken and the Fishery Dept. should issue “Identity Card” to each fisherman. ix) Since both the districts are agricultural based we should interlink agriculture with pisciculture shown in figure-5. Along with pisciculture in ponds other allied culture can be inter-linked in composite farming. The concept of composite farming e.g. in the pond- there are pisciculture and on one side of the pond mulberry trees can be cultivated for the development of sericulture industry-from there silk industry can be grown. Thus the final products (i.e. silk yarn and silk cloth) come to the market and their waste materials are drained off to the pond and used as fish feed. In the same pattern on another side of the pond animal husbandry can be practiced (e.g. poultry, duccary and piggery). Their waste can be used as a valuable manure for fish feed and the residual can be utilized for agricultural production. The excreta of the animal husbandry can also be used in the bio-gas plant for fuel and light. The products of animal husbandry e.g. milk, meat and egg come to the market directly. On another side of the pond some fruit plants such as papine, guava, mango etc. can be cultivated by using the excess manure of animal husbandry and the products can be sold in the market. x) Composite fish culture: Stocking of various species should be in a certain proportion such that various types of fishes live in various layers and eat the entire food organism (so called Polyculture or Composite Fish Culture). Stocking is an important factor to raise fish production. On an average the survibility of stocking is 80% (Jhingran V.G. 1991). This means that 20% of stocking is lost for various reasons such as improper handling, netting and also eaten by preratory animals like snakes, frogs etc. The main species cultured in India are Rahu, Catla, Mrigel, Silver Carp (S.C.), Grass Carp (G.C.), Cyprinus Carpio (Cy, Ca.) 22
  • 23. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 and Bata etc. In polyculture (where various fishes are cultured simultaneously) (Agarwal S.C. 1990) system all these species are cultured in certain ratios. It is found that all of these species do not generally live in the same layer. In mixed (or polyculture or composite culture) culture all these fishes are cultured in such a way that all layer’s feed are used. If we divide the whole layer into 3 parts i.e. upper, middle and lower layers, then Cattla and S.C live in the upper layer, Rahu and G.C. live in the middle layer and Mrigel and Cy. Ca. live in the lower layer. It is also to be noted that these two species in the same layer are not competitors of each other. In their feed habit one eats waste weeds and the other eats green weeds. Hence in polyculture all the pond’s feed are used scientifically and economically. The production of Big head and Big head African giant hybrid magur are totally banned because their food habits are too much and hamper the foods of other common species. Suppose 1000 fish seed is cultivated in the pond. Then the stocking ratios for various fishes are Rahu: Catla: Mrigel: S.C.: G.C.: Cy.Car = 3 : 1 : 1.5 : 1.5 : 1 : 2 Thus the number of seeds are, Rahu = 300, Catla = 100, Mrigel = 150, S.C = 150, G.C. = 100, Cy. Ca. = 200. Now the survibility rate is 80%.Thus the number of produced matured fishes are Rahu = 240, Catla = 80, Mrigel = 120, S.C. = 120, G.C. = 80, Cy.Car. = 160. Fishes are cultivated for 9-12 months. The culture period may extend up to 18 months with partial harvesting between. Moreover, the general annual growth of these fishes are, Rahu = 1kg, Catla 1.5 kg. Mrigel 0.75kg, SC. = 0.8 kg, G.C. = 1.5 kg, Cy. Car. = 0.8 kg. The fish farmers collect fish seeds from private traders or from Government fish farm. xi) Since training is a necessary ingredient to raise fish production the Government has initiated some special training programmes for fish farmers. Moreover to motivate the poor fish farmers some amount of remuneration or stipend is paid to the participant during training. The amount of remuneration depends upon the kind of training, place of training organization and duration of the training programmes conducted by the FFDA. To attend the district level training programmes for the farmers of distant villages, traveling allowances are also paid. xii) It is very essential to use mahua cake because it has dual roles in pisciculture. It firstly acts as a pesticide but later it acts as organic manure and produces a desired quantity of fish food organism for the baby fishes. The presence of ‘Saponin’ in mahua cake kills the pre-ratory animals / fishes of the pond. This poisonous effect continues about 10-15 days and after that it acts as manure. On the other hand, the use of pesticides has a negative long-term effect to ecological balance. So the pesticides are not generally used though they are cheaper. The mahua cake should be applied at the rate of 333.33kg/ Yr. / bigha. 7.2 Limitations of the study The following are the limitations of the study: i) Regarding the change in the level of income before and after the assistance of FFDA, the statements of the fish farmers have been taken into consideration on good faith. ii) Due to limited time, ability and resource constraints, data have been collected from a small number of fish farmers of the two districts and results are assumed to be the representative for the district as a whole. Iii) Due to difficulties in getting responses from the sample fish farmers sometimes we had to rely on the different FEOs’ opinions and also on different official records on good faith. iv) The observations made on the basis of collected data are obviously particularistic in nature in so far as these data relate to micro-study like the present one (i.e. only 100 fish farmers from the two districts were taken into consideration). Micro-studies do not attempt to build general theories but the utility of this type of study is that large number micro-studies may, in course of time, be helpful in constructing meaningful generalizations. Moreover, it is an explorative study which seeks to explore the conditions of fish farmers 23
  • 24. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 before and after the assistance of FFDA programme. A much larger study may be undertaken to vindicate the results obtained from this explorative study. 8. Conclusion The study indicates that the introduction of the programme of FFDA had a clear positive impact on the rural economy through employment and income generation and also raising the standard of living and socio-economic performances of the rural community of the two districts. It is environmentally viable, sustainable and eco-friendly in nature. So for sustainable use of the scarce water resources it is very essential in the present context. Unemployment is a curse for the society today and pressure of population on agricultural sector is rising day by day. Thus fish farming may be an alternative occupation for those unemployed people and undoubtedly generate income in a viable method. Therefore it is recommended that the present programme should be further spread in the rural areas by means of proper planning, adequate supervision, effective implementation and better monitoring in a sustainable manner. References: Agarwal S.C. (1990), Fishery Management, Ashis Publishing House, New-Delhi. Bairagya R. and Bairagya H. (2011), “Water Scarcity a Global Problem- An Economic Analysis”, Indian Journal of Landscape Systems and Ecological Studies, Vol.-34, Institute of Landscape, Ecology & Ekistics, Kolkata, 127-132 Bairagya R. (2004), “A Comparative Study of the Functioning of Fish Farmers Development Agency (FFDA) in the Districts of Burdwan and Birbhum (1985-1995)”, PhD Thesis, Department of Commerce, the University of Burdwan. Bureau of Applied Economics, Burdwan (1998), Key Statistics of Burdwan, Govt. of W.B. Bureau of Applied Economics, Birbhum (1998), Key Statistics of Birbhum, Govt. of W.B Chakraborty S. K. (1991), A Study in Bhery Fisheries, Agro-Economic Reaserch Centre, Visva- Bharati. Dutt R. and Sundharam K.P.M. (2009), Indian Economy, S. Chand and Co. New-Delhi, 101-103 Jhingran V.G. (1991), Fish and Fisheries in India, Hindustan Publishing Cor.-Delhi. Giriappa S. (ed) (1994), Role of Fisheries in Rural Development, Daya Publishing House, Delhi. Gujarati D.N. (2009), Basic Econometrics, McGraw-Hill., 98-133 Mamoria C.B. (1979), Economic & Commercial Geography of India, Shiblal Agarwala Company, Agra-3 Mishra S. (1987), Fisheries in India, Ashis Publishing House, New-Delhi Acknowledgement: In preparation of this paper I deeply acknowledge my respected sir Prof. Jaydeb Sarkhel, Department of Commerce, Burdwan University, West Bengal, India, e-mail: 24
  • 25. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 jaydebsarkhel@gmail.com Tables and Figures: Figure-1 25
  • 26. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 26
  • 27. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Figure-2 Figure-3 Figure-4 Table -1 Change of income distribution of Burdwan. 27
  • 28. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Table -2 Change of income distribution of Birbhum district Table--3 Classification of sample fish farmers according to their monthly incomes after the assistance 28
  • 29. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 Table- 4 Classification of sample fish farmers according to their monthly incomes after the assistance Source: All tables data sources are computed from field survey. 29
  • 30. Journal of Economics and Sustainable Development www.iiste.org ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.2, No.6, 2011 MARKET Light, Fuel Silk yarn & Gobar gas Silk cloth Milk, Meat & Egg Silk industry Cow, Piggary, waste materials Duccary, Poultry w as Sericulture te m at er ia Agricultural production ls Produced Fish (Fruit, papaine, guava, Mulburyculture mango etc.) Side POND Side Circular flow of pisciculture and other allied culture Figure: 5 30