An Assessment of the Effect of Price, Policy and Climate

Changeability on the Supply of Domestic Rice in Sierra Leone: A

Conteh, Alhaji Mohamed Hamza1

and Yan Xiangbin1

1 Management Science and Engineering, School of Management, Harbin Institute of Technology, 150001,

Harbin, China

Abstract. Rice is the foremost and staple food crop in Sierra Leone, it is cultivated in all the regions within

the country. Thus, its domestic cultivation has a noticeable implication for food security as well as self-

sufficiency. It is against this context that, this study assesses the effect of price, policy and climate

changeability on the supply of domestic rice using supply response technique. Data on yield, area and prices

of rice crop during the past years were retrieved from the Food and Agricultural Organization database

(FAOSTAT, 2012).Others were collected from government agencies in Sierra Leone. The Error Correction

regression Model within a cointegration structure was utilized to test the response of rice supply regarding

the factors measured. The study discovered that rice supply in Sierra Leone has a marginal response to price,

weather and policy directives. Nevertheless, area cultivated significantly impacts rice supply in the country.

Due to the aforementioned, it was recommended that the expansion of farm lands through land reforms

system will further increase rice supply. Furthermore, frequent utilization of improved agricultural inputs

through extension delivery system and the accessibility of these inputs by all farmers are proper procedures

of increasing rice cultivation in the country.

Keywords: Price, Rice, Supply Response model, Sierra Leone

1. Introduction

Rice grain is Sierra Leone’s staple and leading food crop, which is cultivated in all the provinces in the

country. Sierra Leone has a huge amount of arable land that is available for rice cultivation in all the four

different ecologies (mangrove swamps, inland valley swamps [IVS], uplands and bolilands). During the

2010 cropping season, approximately 545,500 hectares were under rice cultivation with an assessed domestic

production of 1.03 million metric tons[1]. Johnson et al[2] similarly stated, that rice yield during the same

year was 18,700Hg/ha, which is a reduction of 935,650 metric tons relative to 2006 and an increase of 484,

670 metric tons relative to 2004. While the domestic production in 2011 was approximately 1.08 million

metric tons, the cultivated area was approximately 565,650 hectares and the total yield was approximately 17,

850 700Hg/ha [2]. However, due to the rapid growing population in both rural and urban regions and

increasing per capita consumption, there has been a corresponding increase in the consumption of rice grain

and hence, an increase in its domestic demand during the past decades.

Rice is the second most widely grown cereal crop and the staple food for more than half the world’s

population [3]. It is eaten by about three billion people and is the most common staple food of the largest

number of people on earth [4]. In Sierra Leone generally, as the demand for rice grain[5] has been increasing

by 1,368,000 (mt) yearly between the period 1980 and 2011, the domestic rice supply is only 992,000 (mt),

hence, this indicates that the government must import the difference to meet the domestic requirements[6].

For instance, the average domestic demand for rice is 700,078 metric tonnes whiles the average rice supply is

Corresponding author. Tel.: + 8618745735938.

Email address: hadjmannc90@yahoo.con

2013 International Conference on Agriculture and Biotechnology

IPCBEE vol.60 (2013) © (2013) IACSIT Press, Singapore

DOI: 10.7763/IPCBEE. 2013. V60. 16

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Supply Response Model Approach

only 309,620 metric tonnes within the same period (1980-2011). Therefore, the government needs to embark

on rice importation to meet the requirement of its growing population.

The country imports over 120,000 metric tons of rice yearly as a result of a shortfall in its cultivation.

The price of rice has increased from US$225 per metric ton (both cost and freight) four years ago to a little

over US$ 505 in April, 2008. It is currently between US$ 900 and US$ 1000 in some regions depending on

the quality of grain. At current global market prices[7], this indicates that Sierra Leone may spend about

US$ 120,000,000 on rice importations alone. This rice imports represent a considerable foreign exchange

expense for the economy of the country. However, due to the amount of imported rice and its corresponding

cost, there is already a policy in place to drop rice imports by supporting local rice cultivation.

Even though the country has a huge arable land area to cultivate this most important crop, which almost

every farmer in the country is vigorously involved in cultivating it, thus far, the potential of rice yield has not

been completely achieved.

Nevertheless, the risk in addition to insecurity encountered by agricultural organizations is higher than

those confronted by other normal organizations and so far, the risk aspects are frequently ignored in

the assessment of supply response. Correspondingly, various agricultural researches, projects and policies in

Sierra Leone for instance, government’s concentration on improved varieties, import embargos,

fluctuating tariff on imported rice, rice projects, seed multiplication, international NERICA rice

propagation scheme, import subsidy policies and the political inventiveness on rice have all been

implemented in Sierra Leone previously. Thus far, domestic rice supply has not match with the local demand

of the growing population and subsequently, rice importation still continues

1.1. Objective and Hypothesis of the Study

Consistent with the above-mentioned and for both price and non-price factors, a supply response model

for rice in Sierra Leone is use in this study to achieve the estimates. However, the hypothesis to be test is as

follows:

That the supply of rice in Sierra Leone is not dependent on Price and non-price factors

2. Methodology

Data on yield, area and prices of rice crop during the past years were retrieved from the Food and

Agricultural Organization database (FAOSTAT, 2012).Others were collected from government agencies in

Sierra Leone such as the Ministry of Agriculture, Forestry and Food security[8], the Sierra Leone

Agricultural Research Institute (SLARI), Statistics Sierra Leone (SSL) and the International Institute of

Tropical Agriculture (IITA). Furthermore, the study sourced information from published papers in reputable

journals. The data set covers form 1983-2008 period. Price was deflated with the approved exchange rate.

The improvement that has occurred in time series models shows that it is necessary to follow specific

steps, such as inspecting the statistical properties of the series involve and if essential integrating them in the

ultimate model to confirm the non-spurious regression[9]. To classify the order of integration of all the

variables, the Augmented Dickey Fuller test[10] was estimated to check for the presence of unit root[11] in

the variables, in the event that they do not match the fitted autoregressive process[12]. Variables were

differenced[13] further and until all become stationary[14]

2.3. Engle and Granger Error Correction Technique

Engle and Granger[15] maintained that if there is cointegration between the series[16], the vector error-

correction model can be used[16]. Hence, this study utilized the same model which has the follow equations:

Cointegrating regression Estimation

11 tt X

From equation (1) the estimate which is the residual term can be derive as

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2.1. Data Collection

2.2. Analytical Methods and Test of Stationerity

21̂

tt X

The residual, which is an error correction term is integrated in the short –run equation, and can be

express as:

311 ttt X

Where π, θ, λ and δ are coefficients.

Engle and Granger [15] methodology for testing the co-integration is used to test for stationarity[17]

of the stochastic residuals produced in the equation (2), which was estimated by means of the least square

regression(LSR). Similarly, we estimated the parsimonious error correction model[18].

2.4. Description of the Model

Output = Ƴ (Area, Import, Rain, Policy, Price, Fercons and U) where

Output is the quantity of rice Supply in time t (Rice Output in metric ton)

Area is the cultivated area in time t (Ha)

Import is the quantity of rice imported in time t (substitute for importation policy)

Rain is the amount of rainfall in year t (climate element in mm)

Policy is policy Variable (0 = No policy intervention period and 1 = Policy intervention period)

Price is the producer price of rice in time t (Leone/tons)

Fercons is the quantity of fertilizer consumption in time t (tons)

εt is the stochastic disturbance

U is the error correction term

The parameters to be estimated in equation (2) are π, θ, λ and δ.

Here, 1985 is the policy period since it was the time government intervened in the agricultural sector (by

providing incentives to farmers)

3. Results and Discussion

3.1. Unit Root Test

The results of the Augmented Dickey Fuller[19] or ADF unit root analysis is shown in Table 1.

Table 1 discloses that rainfall and yield were stationery at their levels, hence, the unit root null

hypotheses (δ =1) cannot be accepted at both the 1% and 5% significance levels respectively. Also, the

cultivated area, output, fertilizer consumption, import and price has a unit root. The null hypotheses of the

presence of unit roots[20] in the variables (δ =1) cannot be rejected at the 1% level of significance. However,

all the variables become stationery[21] at first difference suggesting that they are integrated at order one [22]

which is 1- I(1).

Table 1. Result of Augmented Dickey-Fuller, or ADF Unit-root test. Variable Level First difference Integration order

Trended

UnTrended

Trended

Untrended

Output -2. 192 0.7132 7.632* -7.245 I(1)

Yield 3.654** -4.213** I(0)

Area -3.001 0.665 8.541* -8.332* I(1)

Import -3.109 -0.891 -5.432* -5.222* I(1)

Rain 4.276* -6.023* I(0)

Price 4.165** -3.675 9.631* -9.571* I(1)

Fertcons -2.110 -2.244 -6.711* -5.823* I(1)

***significant at 1%; ** significant at 5%; * significant at 10%

3.2. Test for Cointegration

Table 2 indicates that the linear estimation for the relationship between supply (output) and the

explanatory variables results in a stationery process. The null hypothesis cannot be accepted at the 1%

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significance level. Therefore, it could be suggested that, rice output could display a long run equilibrium with

the cultivated area, fertilizer consumption, quantity of rice imported and producer price.

Table 2. Test of Residual

ADF test statistic

Significance Level

T-Statistic

-4.533

Prob.* 0.006

Critical values 1% -4.321

5% -3.654

10% -3.307

Table 3. The Long Run regression Model for Rice Supply Variable Coefficient Std. Error T-Statistic Prob

Price -0.065 0.207 -0.402 0.724

Import 0.154 0.114 1.616 0.141

Area 3.004 0.125 19.192 0.001

Fercon 0.620 0.501 1.832 0.064

C -166.381 84.198 -1.593 0.100

R-Squared 0.982 Mean dependent variable 1986.008

Adjusted R- Squared 0.972 S.D. dependent variable 1622.350

SE.of Regression 311.154 Akaike information criterion 16.189

Sum squared residual 2956961. Schwarz criterion 16.301

Long likelihood -298.007 F-statistic 286.499

D-W stat 0.986 Prob(F-statistic) 0.000

Table 4. The Short Run regression Model for Rice Supply Variable Coefficient Std. Error T-Statistic Prob

LOutput_1 0.154 0.140 0.873 0.362

LPrice -0.023 0.158 -0.076 0.834

Lprice_1 -0.061 0.192 -0.302 0.787

LImport -0.082 0.188 -0.632 0.584

LArea 2.053 0.212 6.734 0.001

Fercon 1.011 0.650 1.489 0.145

LRain -0.109 0.272 -0.823 0.510

LPolicy -192.333 257.030 -0.645 0.492

U_1 -0.534 0.148 -5.198 0.000

C -6.205 40.982 -0.156 0.773

R-squared 0.826 Mean dependent variable 112.012

Adjusted R-squared 0.724 S.D. dependent variable 362.340

S.E. of regression 304.801 Akaike info criterion 14.652

Sum squared resid 1576451 Schwarz criterion 15.233

Log likelihood -295.801 F-statistic 7.798

Durbin-Watson stat 1.961 Prob(F-statistic) 0.000

Table 5. The Short run Parsimonious regression Model for Rice Supply Variable Coefficient Std. Error t-Statistic Prob.

L(AREA) 1.919 0.235 7.989 0.000

L(FCON) 1.576 0.675 2.301 0.034

ECM(-1) -0.615 0.134 -5.710 0.000

C 8.230 33. 451 0.245 0.777

R-squared 0.576 Mean dependent variable 112.201

Adjusted R-squared 0.604 S.D. dependent variable 370.121

S.E. of regression 256.171 Akaike info criterion 14.522

Sum squared residual 1854101.34. Schwarz criterion 14.612

Log likelihood -333.541 F-statistic 29.172

Durbin-Watson stat 1.976 Prob(F-statistic) 0.000

Table 3 shows that the output model is fit with Log likelihood score of -298.007 and F-Statistics of

286.499 at the 1% significance level. The Akaike information as well as Schwarz criterion values (16.189

and 16.301respectively) were low, which further emphasizes that the model is well fit. The R- squared value

suggests that the explanatory variables included jointly explain about 98 % of the variation in rice output.

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3.3. Model for the Long Run

However, cultivated area was establish significant in influencing the supply of domestic rice in the long

run, the cultivated area was significant at 1 % and fertilizer consumption rate at the 10 % level. Many

researchers such as Seck [23] and Due[24] had established that, rice supply frequently respond to the

cultivated area in the long run[25]. The coefficient of rice output is larger than one (3.004), and in terms of

area therefore, supply is said to be elastic. The domestic supply of rice fails to respond to price factor as well

as policy regulation for the period measured in the long run[25]. The failure of price to respond to rice

supply supports the findings of Hidayat et al [26], Hochman et al [27] and Lagi[28].

The short run result in Table 4 also points out that, the area cultivated is the most important factor

influencing rice supply in Sierra Leone. The coefficient of lagged cultivated area was establish to be

significant at 1 %, the coefficient of adjustment was as well significantly large (2.053). The rate of

adjustment of rice output to shocks caused by area cultivated is therefore faster. However, after

parsimoniously estimating the model, the coefficient of fertilizer consumption was insignificant at 5 % level.

The log likelihood score was -295.801 and the F-value was establish to be significant at the 1 % level, the

explanatory or independent variables jointly explain approximately 83 % of variation in rice output in all

the models. Additionally, the Akaike information as well as Schwarz criterion(14.652 and 15.233

respectively) were very low and hence, cannot endorse the error correction model fitness[29]. For all the

models, the error correction term[30] was establish to be significant at 1 % level(Table 5) and it has the

expected negative sign suggesting an error correction in the short run[31].

Due to the increasing import costs and following the huge loss on foreign exchange reserve, which are

compelled by the continuous growing demand and scarcity in the supply of locally cultivated rice grain,

this study however, estimate the supply response model for the Sierra Leone rice situation. From a thorough

statistical assessment, the area cultivated was noted as the most serious factor upsetting supply response of

rice in Sierra Leone. Therefore, with the purpose of improving the supply of rice grain in Sierra Leone, the

subsequent recommendations are submitted for policy action.

Expansion of farm lands through land reforms system will further increase rice supply in Sierra

Leone.

Imports and price factors in addition to government policies on agricultural sector are not effective

methods in motivating the supply of rice in country and thus, should be reviewed.

Furthermore, frequent utilization of improved agricultural inputs through extension delivery system

and the accessibility of these inputs by all farmers are proper procedures in increasing rice cultivation

in the country.

5. Acknowledgements

We thank the Harbin Institute of Technology (HIT) for providing grants for this research. We are also

thankful to the corresponding institutions of Sierra Leone for providing the data. We appreciate the insightful

comments and suggestions from the reviewers of this paper at the manuscript phase

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