What Is Oil Palm Production Commerce Essay

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Oil palm productions are considered as a major component due to the increasing of economic growth in Malaysia and Indonesia. This paper analyzes the efficiency of factor production of oil palm or fresh fruit bunch (FFB) production as dependent variable for both countries by looking to the total area planted, total labour employed and yield per hectare as independent variable using Ordinary Least Square (OLS) method. The data will be test by using Log-Log regression. In this study, the data were collected in the Malaysia Statistic of Department, Malaysia Economic Planning Unit, Indonesia Statistic and Food and Agriculture Organization of the United Nations. The period of study range from 1975 to 2006. In multiple regression model for Malaysia, only total area planted is significant impact to the oil palm production while for Indonesia, total area planted and total labour employed are significant affect the oil palm production.

Khairil, et.al.(2010) in their study said that Malaysia and Indonesia palm oil plantations are the major commodity producer with Indonesia currently is the largest producer followed by Malaysia being the world's second-largest area of oil palm. This is supported by Parkhomenho in his study that Malaysia and Indonesia are major producers of palm oil in the world. In 2000, the total oil produced is 23 million tones and 82% were contributed jointly by Malaysia and Indonesia. Malaysia consumes 80% of palm oil produced for export, while Indonesia is only 30% use palm oil production for export and the rest used for domestic market.

Malaysia currently accounts for 39 % of world palm oil production and 44% of world exports. If taken into account of other oils & fats produced in the country, Malaysia accounts for 12% and 27% of the world's total production and exports of oils and fats. Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably.

In Indonesia, the oil palm sector to grow rapidly through the expansion of the total area of oil palm plantations in 2008 to 2009 which is from 7 million to 7.3 million hectares. While crude palm oil (CPO) production grows from year to year which is 19.2 million tons in 2008 to 19.4 million tons in 2009. In 2008, the exports of CPO are 18.1 million tones. This value increased only in the first nine months of 2009, CPO exports reached 14.9 million tons and in 2009, Indonesia has become a top palm oil producer with production of 19.4 million tones.

On the above study, the objective of this study is to determine the factor that affect oil palm production which is the factors that affect most of oil palm production in Malaysia and Indonesia.

2. Literature Review

2.1Fresh Fruit Bunch (FFB)

According to Malaysia Palm Oil Council (2011), in Malaysia, oil palm trees are planted mainly of tenera variety, a hybrid between the dura and pisifera. Oil palm trees can survive and productive to produce fruit for up to 20 to 30 years, and will start fruting within 30 months after planting. Each ripe bunch is known as fresh fruit bunch (FFB). Ramasamy, et.al.(2005) study that Malaysia is the world's main exporter and producer of palm oil, which is contributed approximately 50% in 2002 of world palm oil production. Meanwhile, exports it is about 58%. In 2002, from the 3.67 million hectares of oil palm which is planted in Malaysia, 60% were under private ownership, the majority of which are run by firms in the private sector.

2.2Area

Area is one of the important factors on oil palm production. Based on Ramasamy, et.al. (2005), in the last two decades, the private sector played important role to drive for growth which is in the production and the research and development of palm oil. In 1980 to 2002, the planted area which is under privately-owned plantations firms had increased from 557659 hectares to 2,187,750 hectares. The largest part of these developments is being in the states of Sabah and Sarawak. Koczberski and Curry (2005) in their study found that the regulation specify the areas planted to oil palm and the leases are over fixed land areas, and also kept for food production, though this latter limit has not been enforced. In feedback to such pressures, the several strategies have been developed by the smallholders to secure their livelihoods, including non-oil palm livelihood strategies and new oil palm production practices.

Fairhurst and Mutert (1999) in their study said that the worldwide area planted to oil palm has increased more than 150 percent over the past 30 years. The Southeast Asia is the largest part that has taken place of these increases, with spectacular production increases in Indonesia and Malaysia. Basri and Zaimah (2002) in their study said that the results indicate that in determining the total area of oil palm in Malaysia, the change in either palm oil or natural rubber price is not very important even in the long-run. This can be due to the existence of right land constraint.

3. Data and Methodology

In this paper, annual data were used obtained from the Food and Agricuture Organization of the United Nations, Malaysia Economic Planning Unit, Malaysia Department Of Statistic and Indonesia Statistics. The data was covering in time series data from year 1975 to 2006 that is 32 years. For the population, this paper focuses on Malaysia and Indonesia oil palm industry. The data was taken about the total of oil palm production, total area planted hectarage and yield per hectare and were estimated using ordinary least square (OLS) which is including simple and multiple regression.

3.1 Data Analysis

The E-Views programme has been used to run the regression of the data. The unit root test is used to analyze the data to fulfill at stationarity of the variables. Ordinary Least Square also will be used to analyze and interpret the data. All dependent and independent variable are transformed to logarithm to fit the data.

3.1.1Unit Root Test

The data will be analyzed using unit root test before the data estimate by using ordinary least square. This is to test the data stationarity of the variables.

3.1.2 Ordinary Least Square

This is the method that used to estimate the unknown parameters in the linear equation. It consist simple and multiple regression models. Simple regression model is a least square that have one independent variable. In other words, it fits the straight line to minimize the sum of squared as small as possible. Multiple regression models are a least square that used to know the relationship between several independent variables or variable predictor. Both of model regressions are transformed into logarithm. The model regression for Malaysia was transformed into log-log. The formula is shown below:

Area

lnProduction = β0+ β1lnArea + µ ……………………………………….……..(1)

Labour

lnProduction = β0+ β2lnLabour + µ ………………………………………..….(2)

Yield

lnProduction = β0 + β3lnYield + µ ……………………………………………..(3)

However, in order to reduce multicollinearity (perfect collinearity) in model regression for Indonesia, the model regression was transformed into double log. The new equation for Indonesia is shown below:

Area

lnlnProduction = β0 + β1lnlnArea + µ …………………………………………(4)

Yield

lnlnProduction = β0 + β1lnlnYield + µ ………………………………………...(5)

For the multiple regressions, this study involves Area, Labour and Yield as independent variable. The multiple regressions for Malaysia are shown below:

lnProduction = β0 + β1lnlnArea + β2lnlnLabour + β3lnlnYield + µ …………(6)

However, cause by the data of labour is not available in Indonesia, this study have to omit that variable (labour). The multiple regressions for Indonesia are shown below:

lnlnProduction = β0 + β1lnlnArea + β2lnlnYield + µ ………………………….(7)

4. Hypothesis Development

4.1 H0: There is no relationship between FFB production and total planted area

H1: There is a relationship between FFB production and total planted area

4.2 H0: There is no relationship between FFB production and labour input hectare

H1: There is a relationship between FFB production and labour input hectare

4.3 H0: There is no relationship between FFB production and yield per hectare

H1: There is a relationship between FFB production and yield per hectare

4.3 H0: There is no relationship between FFB production with total planted area, labour input and

yield per hectare

H1: There is a relationship between FFB production with total planted area, labour input and

yield per hectare

5.Finding

In this section, the finding of the study will be analyzed. This is including the significant between the variables, the relationship and the interpretation and also the regression problems. The data will be analyzed by using the E-view program to run a regression.

Before constructing the equation of regression model, a stationarity test was conducted because the data came from a time series. Unit root test was conducted in order to check the stationarity of the time series data of all the variables. Unit root test, applying the Augmented Dickey-Fuller (ADF) test, was used to test whether the variables were stationary or not.

Table 1: ADF Unit Root Test result of time series variables for FFB Production, Area, Labour

Input and Yield Per Hectare for Malaysia

Variables

ADF- Statistics

Critical Values

Order of Integration

FFB Production

-8.599873

(0.00001)***

1% = -3.670170

5% = -2.963972

10% = -2.621007

1st difference of integration

Area

-6.521268

(0.00001)***

1% = -3.689194

5% = -2.971853

10% = -2.625121

2nd difference of integration

Labour Input

-4.940815

(0.0005)***

1% = -3.711457

5% = -2.981038

10% = -2.629906

2nd difference of integration

Yield Per Hectare

-5.946767

(0.00001)***

1% = -3.661661

5% = -2.960411

10% = -2.619160

1st difference of integration

Notes: The value at the parenthesis is probability

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 2: ADF Unit Root Test result of time series variables for FFB Production, Area and Yield

Per Hectare for Indonesia

Variables

ADF- Statistics

Critical Values

Order of Integration

FFB Production

-5.951992

(0.0002)***

1% = -4.323979

5% = -3.580623

10% = -3.225334

2nd difference of integration

Area

-4.462487

(0.0068)***

1% = -4.296729

5% = -3.568379

10% = -3.218382

1st difference of integration

Yield Per Hectare

-6.191384

(0.0001)***

1% = -4.296729

5% = -3.568379

10% = -3.218382

1st difference of integration

Notes: The value at the parenthesis is probability

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 1 and 2 shown the results of the Augmented Dickey-Fuller (ADF) classes of unit root tests for Malaysia and Indonesia. The tests were applied to each variable over the period of 1975-2006 with a time trend at the variable level and at the stationary of different.

Table 1 that is the result of the unit root test for Malaysia shows that FFB Production and Yield Per Hectare is stationary at the first difference. The Area and Labour Input is stationary at the second difference.

Table 2 that is the result of the unit root test for Indonesia shows that FFB Production is stationary at second difference. The Area and Yield is stationary at the first difference.

Table 3: The simple log-log estimated of Area, Labour and Yield equation over the period 1975-2006 for Malaysia

MODEL

lnProduction = β0

+ β1lnArea + µ

(MODEL 1)

lnProduction = β0

+ β2lnLabour + µ

(MODEL 2)

lnProduction =

β0 + β3lnYield + µ

(MODEL 3)

Constant

5.503360

-2.590924

-1.189230

Coefficient

1.506942

(34.34895)

1.634885

(17.29135)

6.218152

(2.503615)

R-squared

0.966886

0.847965

0.185476

Adjusted R-squared

0.965783

0.842897

0.158326

Akaike info criterion

-0.781998

0.742164

2.420659

F-statistic

875.9697

167.3234

6.831343

p-value

0.0001***

0.0001***

0.0180**

Durbin-Watson stat

0.674553

0.154765

0.385122

Notes: The value at the parenthesis is t-statistics

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 3 shows that all variable that is area, labour and yield are positive correlation with FFB production.

For area, every 1% increases in area, FFB production will increases 1.506942%. The R2 of 0.96 means that 96% dependent variable can be explained by independent variable while 4% of dependent variable cannot be explained by independent variable.

For labour, every 1% increases in labour, FFB production will increases 1.634885%. The R2 of 0.84 means that 84% dependent variable can be explained by independent variable while 16% dependent variable cannot be explained by independent variable.

For yield, every 1% increases in yield, FFB production will increases 6.218152%. The R2 of 0.18 means that 18% dependent variable can be explained by independent variable while 82% dependent variable cannot be explained by independent variable.

Table 4: The multiple log-log estimated results of Area, Labour and Yield over period 1975-

2006 for Malaysia (MODEL 6)

Particulars

Values

Variables:

lnArea

lnLabour

lnYield

Coefficient

1.765193 (0.0001)***

-0.360689 (0.0556)*

0.991747 (0.0421)**

Constant

4.966383

R-squared

0.975089

Adjusted R-squared

0.972420

F-statistic

365.3383

P-value (F-stat)

0.0001

Akaike info criterion

-0.941647

Durbin-Watson stat

0.615622

Notes: The value at the parenthesis is probability

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 4 shows that the R2 of 0.97 is high, thus it explained that 97% of the variation in the value of oil palm production. It means, there are 97% of FFB production can be explained by area, labour and yield. 3% of FFB production cannot be explained by area, labour and yield.

If the total area increases by 1%, FFB production increases by 1.765193%. If the labour increases by 1%, FFB production decreases by 0.360689%. If yield increases by 1%, FFB production increases by 0.991747%. The Durbin-Watson stat 0.61 shows that there is an autocorrelation occurs.

Table 5: The simple log-log estimated of Area and Yield equation over the period 1975-2006 for

Indonesia

MODEL

lnlnProduction = β0

+ β1lnlnArea + µ

(MODEL 1)

lnlnProduction = β0 +

β3lnlnYield + µ

(MODEL 2)

Constant

0.616501

-3.091895

Coefficient

0.837503

(63.21210)

2.360656

(1.568637)

R-squared

0.992548

0.075803

Adjusted R-squared

0.992300

0.044997

Akaike info criterion

-7.339092

-2.518649

F-statistic

3995.769

2.460623

p-value

0.0001***

0.1272

Durbin-Watson stat

0.971694

0.058799

Notes: The value at the parenthesis is t-statistic

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 5 shows that all variable that is area and yield have a positive correlation with FFB production.

For area, every 1% increases in area, FFB production will increases 0.837503%. The R2 of 0.99 means that 99% dependent variable can be explained by independent variable while 1% dependent variable cannot be explained by independent variable.

For yield, every 1% increases in yield, FFB production will increases 2.360656%. The R2 of 0.07 means that 7% dependent variable can be explained by independent variable while 93% dependent variable cannot be explained by independent variable.

Table 6: The multiple log-log estimated results of Area and Yield over the period 1975-2006 for

Indonesia (MODEL 7)

Particulars

Values

Variables:

lnlnArea

lnlnYield

Coefficient

0.823373 (0.0001)***

0.752742 (0.0001)***

Constant

-1.222666

R-squared

0.999973

Adjusted R-squared

0.999971

F-statistic

536875.9

P-value (F-stat)

0.0001

Akaike info criterion

-12.89672

Durbin-Watson stat

1.141870

Notes: The value at the parenthesis is

***significant at 0.01 **significant at 0.05 *significant at 0.10

Table 6 shows that the R2 of 0.99 is high, thus it explained that 99% of the variation in the value of oil palm production. It means, there are 99% of FFB production can be explained by area and yield. 1% of FFB production cannot be explained by area and yield.

If the total area increases by 1%, FFB production increases by 0.823373%. If yield increases by 1%, FFB production increases by 0.752742%. The Durbin-Watson stat 1.14 shows that there is an autocorrelation occurs.

Figure 1: Regression line analysis for Malaysia Figure 2 : Regression line analysis for Indonesia

6. Conclusion

This study is about the analysis of factor that most affect the oil palm production. When conducting the unit root test, all variable are stationary. For Malaysia, the FFB Production, and Yield per Hectare are stationary at first difference while Area and Labour stationary at second difference. For Indonesia, only F FB production stationary at second difference while Area and Yield per Hectare are stationary at first difference.

When conducting the simple regression analysis, all variables are affecting the FFB production. The factor that more significant and affect most of FFB production for Malaysia and Indonesia is Area. It is because, the area variable have a high value of t-statistic and F-statistic. However, in multiple regression analysis, the result indicates that Malaysia and Indonesia have an autocorrelation problem. It can be detected by looking at the Durbin-Watson statistic. Malaysia and Indonesia have a lower value of DW stat. Nevertheless, this problem can be solved through the ADF test by looking at its order of integration.

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