Determinants of Paddy Field Conversion in Java 1995-2013

Jurnal Bina Praja

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Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina *

Research and Development Center National Land Agency Jl. H. Agus Salim No. 58, Menteng, Central Jakarta

Received: 25 November 2016; Accepted: 24 March 2017; Published online: 31 May 2017 DOI: 10.21787/jbp.09.2017.1-13

Abstract

Java is the biggest rice supplier with the largest paddy field compared to other islands in Indonesia. On the other side, economic growth needs more and more land for infrastructure growth. It was suggested that the growth would convert paddy field in Java to non-agriculture use. This paper reports the growth of land conversion, especially from

paddy field to other utilization, in Java Island using panel data (provincial data 1995-2013). The data then treated as the dependent variable in the regression analysis for determining the factors that are significantly influential. The relative strengths of the influential factors are then compared using the concept of elasticity. It is found that farmers term of trade and GDP of manufacturing industries sectors significantly influence the land conversion. Meanwhile, the size of the population and the size of medium and large companies of manufacturing industries do not significantly influence the

land conversion. Keywords: Paddy Field Conversion, Determinant Factors

I. Introduction 2010). A land is a natural resource that is strategic

A land is a resource that physically cannot be for development. Almost all sectors of the physical produced so that the supply of land is limited. On the

development need land, such as agriculture, forestry, other side, the high demand for land for a variety of

housing, industry, etc.

activities tend to exceed the supply of available land. For agriculture, a land is a resource that is very This condition leads to scarcity of land. Furthermore, important, both for farmers and for agricultural

the scarcity of land leads to the competition of development. It is based on the fact that Indonesia land use and it can cause land conversion. This is still relies on farming land (land-based agriculture

consistent with Barlowe’s description in Butar- activities) (TB, Purwanto, F, & Ani, 2010). Although

Butar (2012) that there is competition in term of the quality of land resources can be improved, land use, because of the imbalance between limited

but the quantity of land resource available in supply and unlimited demand.

each region practically remained the same. On In these conditions, the increase in land

the condition of these limitations, the increasing use requirements for an activity will reduce the

need for land for housing, industry, infrastructure availability of land for other activities that can cause

development, social services, and others will reduce land conversion. Land conversion can be defined the availability of land for agriculture. As a result,

a lot of agricultural lands, especially paddy field, is land use that is different from previous activities.

as a change of land use for an activity into other

converted to non-agricultural use (Hidayat, 2008). Land conversion is a phenomenon that cannot be

Viewed from the economic side, the paddy field avoided either in underdeveloped, developing, or

conversion can be influenced by several factors such developed countries, in line with economic growth

as the farmer’s term of trade. The low term of trade and population growth (Azadi, Ho, & Hasfiati, based on his paddy production, causing no incentive

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field conversion is not anticipated, it is expected to is also thought to be influenced by the growing

have an impact on the food situation in the future. number of industries, particularly large industry,

Based on the exposure above, the study about the in which the development of an industry depends

determinant of paddy field conversion in Java on the availability of land. Moreover, in the creation

becomes important and relevant. of value added, the industrial sector also holds a

Economic growth is characterized by the dominant position. This can be illustrated through

development of industry, economic infrastructure, the increased role of the industrial sector in the GDP

public facilities, and settlements in which all require (Suriyanto, 2012).

land and increased demand for land to meet the The rise of land conversion that occurs on

needs of the non-agricultural. However, there is no agricultural land cannot be separated from the

doubt that economic growth is also improving the Government’s attention. Various efforts made by

socio-economic condition in a non-agricultural land. the Government to ensure food security include the

These conditions are caused by the conversion of protection of agricultural land. One of the Efforts

agricultural land that continues to increase along made by the government to protect agricultural

with the growth and economic development, that land is through a juridical approach by issuing

could not be avoided (Sudaryanto, 2004). policies. However, a research conducted by Irawan,

Based on a research conducted by Pakpahan et. al. (2000) found that the regulations to protect

(1993), at the regional level, the conversion agricultural land are still ineffective enough to

of paddy fields has been indirectly affected by control the conversion of agricultural land into

Changes in the structure of the economy, population other uses. There is also an indication that paddy

growth, urbanization Flow, and consistency on the fields conversion, particularly in irrigated land,

implementation of the spatial plan. Paddy fields is precisely triggered by the mechanism of the

conversion is also directly influenced by Growth in Provincial/District Spatial Plan/RTRW (Isa, 2006).

the construction of transportation facilities, land for The occurrence of paddy field conversion is

industrial growth, growth in residential facilities, not beneficial for the agricultural sector as one of

and distribution of paddy fields. According to the them can lower rice production capacity, while

research conducted by Ilham, Syaukat, & Friyatno about 95% of national rice production results from

(2005), it shows that land conversion factors are Paddy field (Irawan, Purwoto, Dabukke, & Trijono,

grouped into three items, namely: economic factors, 2012). Problems associated with the paddy fields

social factors, and existing land regulations. In terms conversion in Indonesia, of course, are inseparable

of the economy in Indonesia, In the micro level, the from the role of Java as a center of rice production.

development of settlements affects paddy fields Java’s role in national rice production is quite large

conversion, but at the macro level, the development although the extent is only 7% of the total land area

of settlements, proxied by an increasing number of Indonesia. Based on the distribution, Java has the

of people, does not show a positive relationship. largest paddy field, which is approximately 3,231

Meanwhile, based on the macro level, paddy field thousand hectares or 43% of the total area of paddy

conversion is positively correlated with GDP growth

Farmers Term of Trade

GDP of Manufacturing Industries

Paddy Fields Conversion

Numbers of Population

Regulation Numbers of Medium and Large Scale

Companies in Manufacture Industries

Figure 1. Mapping Framework of the Research

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method to describe paddy fields conversion that the occurrence of paddy fields conversion based on

occurred on the island of Java. The analysis is the farmers’ term of trade variable, an industrial

performed using a mathematical formula as follows: development seen from the GDP of the industrial sector and the number of medium and large

(∁ t -A t )=L t -L t-1 companies in the manufacturing industry and also

the population variable. Paddy fields conversion is defined as net paddy To review it, this research is proposing three

fields conversion. This means that paddy fields area main questions. First, how is the growth of paddy field

in year t (Lt) is the paddy fields area of the previous conversion in Java? Second, what is the determinant year (Lt-1) plus the new paddy fields (Ct) and the

factors influencing paddy fields conversion in Java? reduced paddy fields (At). Thus, if the paddy fields And third, how far is the regulations applied in order conversion is positive, it means a new paddy field

to control paddy fields conversion. occurs more comprehensive, respectively in year

Summarizing the above review, the framework t. Conversely, if the paddy fields conversion is of this study can be illustrated as in Figure 1. negative, meaning that paddy fields reduced wider Research hypothesis can be described as than new paddy fields, respectively in year t (Ilham

follows: first, presumed that paddy fields conversion

et al., 2005).

in Java is increasing. Second, the farmers’ term of trade, the GDP of manufacturing industries, numbers of the population, and numbers of medium and

B. Statistical Analysis

In accordance with the objectives and large scale companies in manufacturing industries with significant influence towards paddy fields hypothesis of the research, the relationship between

conversion in Java. variables can be described specifically in multiple

This is based on previous research about the linear regression models (Juanda & Junaidi, 2012). This analysis can be used to describe the extent of

factors that affect paddy field conversion, one of reliance is the dependent variable by one or more

which is the research conducted by Kustiawan (1997) which stated that the external factors that influence independent variables. Based on the variables that

the occurrence of paddy fields conversion are the described the multiple linear regression models, it

was formulated as follows:

regional development, population growth and GDP growth. The greater the rate regional development, the higher the rate of population growth and the

Y=a+b 1 X 1it +b 2 X 2it +b3 X 3it +b 4 X 4it +e t

greater the rate of GDP growth resulted in extensive increases of paddy field conversion. Information:

= The rate of paddy fields conversion

II. Method growth to provincial -i, the time period

-t (%)

X 1 = The rate of farmers term of trade The data consists of data on the size of paddy fields,

The data used in this study is secondary data.

growth to provincial -i, the time period Gross Domestic Product (GDP) in manufacture

-t (%)

industries, farmers term of trade (NTP), numbers

X 2 = The rate of GDP manufacturing of population, and numbers of medium and large

industries growth to provincial -i, the scale companies in manufacturing industries in 5

time period -t (%) provincial regions in Java, namely west Java, central

X 3 = The rate of population growth Java, Yogyakarta, East Java, and Banten with time

provincial -i, the time period -t (%) period 1995-2013. The data is collected by learning,

X 4 = The rate of medium and large number studying, and analyzing the source of literature in

of companies in the manufacturing the form of statistical data from the published data,

industry growth provincial -i, the time books, and scientific journals that are relevant to the

period -t (%)

research.

a = Constanta

b 1 ,b 2 ,b 3 = regression coefficient in order to answer the problems in research. The

The collected data then processed and analyzed

e it = error factors

analytical method used is quantitative descriptive analysis and statistical analysis using multiple

The data used in the analysis is panel data linear regression methods to test the significance of

with the time period of 1995-2013. Based on the variables through F test and partial correlation.

the variations in the formed assumptions, there are three approaches to the calculation of panel

Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina

Growth of Paddy Fields Conversion by Province in Java of 1995-2013 (Ha)

Year West Java

East Java

Banten Java

1995-1997 Total

0 -26.770 conversion

0 -8.923 Average

1998-2000 Total

-385 -203.732 conversion

-128 -67.911 Average

2001-2003 Total

-13.173 -68.657 conversion

-4.391 -22.886 Average

2004-2006 Total

-219 -44.164 conversion

-73 -14.721 Average

2007-2009 Total

2010-2013 Total

1.932 -32.469 conversion

483 -8.117 Average

1995-2013 Total

-11.471 -369.812 conversion

-604 -19.464 Average

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

data regression model (Firdaus, 2011), namely: occurs in all provinces in Java with a total conversion (1) Common-Constant Method (The Pooled OLS

of about 370 thousand hectares, or approximately Method or PLS), (2) Fixed Effect Method (FEM), and

19 thousand hectares annually. This trend indicates (3) Random Effect method (REM). Furthermore, this

that paddy fields conversion is greater than the new equation models are analyzed using the program

paddy fields. This conversion includes conversion of Eviews 7.0 for estimating the determinant factors of

paddy field into non-agricultural activities such as paddy fields conversion on Java in 1995-2013.

industrial or service industries and conversion to agricultural activities other than paddy fields such

III. Results and Discussion as farm or plantation and horticulture.

Based on Table 1, the highest paddy fields

A. The Growth of Paddy Fields Conversion conversion occurred during the year 1998-2000

with total paddy fields converted about 204

in Java

thousand hectares or approximately 68 thousand The growth of paddy fields conversion in this

hectares annually. This shows that in the period of study is analyzed by looking at the changes in paddy

economic crisis, paddy fields conversion increased fields area in each Java province. The growth of

sharply compared to the previous period in which paddy fields conversion in Java in 1995-2013 can be

the paddy fields conversion in 1995-1997, during seen in Table 1. Based on Table 1, it can be seen that

this period paddy field converted about 27 thousand during the year 1995-2013, paddy fields conversion

hectares. Compared to the previous period, the

Jurnal Bina Praja 9 (1) (2017): 1-13 4 Jurnal Bina Praja 9 (1) (2017): 1-13 4

Paddy Field Conversion Rate by Province in Java, the year of

This situation triggered the paddy fields conversion. 1995-2013 Most people who only have assets in the form of paddy fields will sell the land to maintain living to

Paddy Field

those who earn high incomes.

Province

Conversion Rate

Along with the increasing of economic level

at the average net per

post economic crisis, the paddy field conversion is year (%) reduced, it can be seen throughout the year 2001-

2003 which the amount of paddy field conversion 1.07 is 69 thousand hectares reduced about 44 thousand

West Java

Central Java

hectares from the year 2004-2006. In the period

2007-2009 the overall paddy fields conversion even 0.47

Yogyakarta

be positive, meaning that the number of new paddy

fields is higher than the reduced paddy fields. The 0.36 new paddy field occurs about 6 thousand hectares.

East Java

Banten

However, unfortunately, this condition does not

last long, the paddy fields conversion in Java occurs 0.57 again in 2010-2013 and reduced about 32 thousand hectares paddy field or about 8 thousand hectares

Java

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

annually. It indicates that the paddy field conversion still cannot be controlled.

Based on paddy field conversion that occurs 0.47% per year. Third and fourth highest paddy in all province in Java, paddy field conversion in

field conversion rate in Java is occupied by East Java West Java province is higher compared to other

province with net paddy fields conversion rate of provinces. Throughout the Years 1995-2013 West

about 0.36% per year and Banten with paddy fields Java Province experienced paddy field Conversion

conversion rate of about 0.29% per year, while the about 228 thousand hectares or about 12 thousand

last position with lowest paddy field conversion hectares per year. West Java province is also the province which experienced the sharpest increase

of paddy field conversion on economic crisis

Table 3.

period that is about 195 thousand hectares or

Paddy Field Conversion Rate by Regency/City in Java,

increased about 8.5% from the size of paddy field Year 2009-2013 conversion in the previous years. East Java is in the

second position which experienced higher paddy

Paddy Field

fields conversion. During 1995-2013, paddy field Conversion Rate conversion at central Java is about 77 hectares. at the Average Net/Year

Regency/City

That was followed by Central Java at 48 thousand

hectares. Paddy field conversion in Banten Province -17.66 is in the fourth position that the paddy field reduces

Depok City

Bandung City

about 11,5 thousand hectares, and followed by DIY of about 5 thousand hectares.

Cirebon City

The growth of paddy field conversion is also

seen by a paddy field conversion rate. Paddy field -6.17 conversion rate indicates the percentage of paddy

Ciamis City

Tegal City

field conversion speed that occurs in each province

in Java. The rate of paddy field conversion by -5.86 provinces in Java in 1995-2013 can be seen in Table

Kudus City

Cirebon City

2. Based on Table 2, it can be seen that the net

Bogor City

rate of paddy fields conversion in Java over the

period 1995 to 2013 is about 0.57% per year. West -4.31 Java Province experienced the highest rate of paddy

Surabaya City

field conversion compared to other provinces in -3.24 Java, in which the value is about 1.07% per year.

Yogyakarta City

Cilegon City

The second position is experienced by Yogyakarta province with a paddy field conversion rate of about

Source: Statistics Indonesia (BPS), 2009-2013 (processed)

Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina

The Estimation Results Using Pool Ordinary Least Square (PLS) Method

Variable

Coefficient

Std. Error

X1 -0.052569

17.81571 Adjusted R-squared

Prob(F-statistic)

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

rate throughout the years 1995-2013 is occupied by companies in the manufacturing industries. Central Java province at 0.26% per year.

Determination of the independent variables Based on the growth of paddy field conversion

is based on previous research, especially research in all cities/regencies in Java in the last 5 years

conducted by Ilham et al (2005) about the (2009-2013), it shows that in general, Paddy field

development and the factors that affect paddy fields conversion rate is the highest in urban areas, as

conversion in Indonesia, where the independent shown in Table 3.

variable used in this study is also one of the variables Based on Table 3, in the period of 2009-2013,

used in the study. Farmers’ term of trade as a proxy the highest paddy field conversion rate is dominated

for the competitiveness of agricultural products, by Urban area. Depok city experienced the highest

especially rice; GDP of the industrial sector and paddy field conversion rate at 17.66%, followed

the number of medium and large companies in the by Bandung city on the second position with

manufacturing industry as a proxy for industrial 11.76%. third, fourth and fifth position, respectively

development, as well as a population as a proxy for occupied by Cirebon regency (8.76%), Ciamis

residential needs.

regency (6.17%) and Tegal city (5.88%). Table 3 These factors are calculated based on data also shows that the region with the highest paddy

from BPS between 1995 and 2013 from 5 Provinces field conversion is dominated by areas in West Java

in Java by using Eviews 7.0.

province, such as Depok city, Bandung city, Cirebon regency, Ciamis regency, Cirebon city and Bogor

3) Regression Analysis

city (4.68%). The region that experienced highest The model equations are obtained based on paddy field conversion rate in Central Java province

the results of the estimation Pool Ordinary Least is Tegal city and Kudus regency (5.86%). The region

Square (PLS) method, Fixed Effects Model (FEM) experienced highest paddy field conversion rate in

Method and Random Effects Model (REM) Method East Java province is Surabaya city with paddy field

respectively as follows:

conversion rate in 4.31%. Yogyakarta is a region which suffered the highest paddy field conversion

a) Pool Ordinary Least Square (PLS) Method Rate in Yogyakarta province is at 3.24%, While

The model equations are obtained the highest paddy field conversion rate in Banten

based on the results of the estimation province is occupied by the city of Cilegon by 2.15%.

Pool Ordinary Least Square method (PLS) is as follows:

B. Determinant Factors of Paddy Fields Conversion in Jawa

Y=0.145-0.0526X 1 +0.012X 2 +0.140X 3 +0.008X 4 The determinant factors that influence paddy

fields conversion in Java in this study were analyzed The estimation results using Pool based on the effect of the farmers’ term of trade,

Ordinary Least Square (PLS) Method in GDP of manufacturing industries sector, numbers

the form of value coefficients, standard of population and number of medium and large

errors, t-statistics and analysis results are

Jurnal Bina Praja 9 (1) (2017): 1-13 6

The Estimation Results Using Fixed Effect Models (FEM) Method

Variable

Coefficient

Std. Error

X1 -0.053836

0.2351 Cross-section fixed (dummy variables) R-squared

9.106971 Adjusted R-squared

Prob(F-statistic)

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

presented in Table 4. the intercept every region is different, depending on the value of the dummy

b) Fixed Effect Model (FEM) Method (Di). The coefficient of basic, standard The model equations are obtained

errors, t-statistics and analysis results are based on the results of the estimation

presented in Table 5. using Fixed Effect Model (FEM) is as follows:

c) Random Effect Model (REM) Method The model equations are obtained Y=0219+D-0.0543X 1 +0.0119X 2 +0.120X 3 +0.007X 4 based on the results of the estimation

using Random Effect Model (REM) is as In equations using FEM method, the

follows:

slope for farmers term of trade, the GDP of manufacturing industries sector, numbers

Y=0219-0.065X 1 +0.0117X 2 +0.135X 3 +0.002X 4 of population and the number of medium and large companies in the processing

In the equation using the method industries applicable to all regions i, but

of this method, error component of each

Table 6.

The Estimation Results Using Random Effect Models (REM) Method

Variable

Coefficient

Std. Error

X1 -0.064911

2.491596 Adjusted R-squared

Prob(F-statistic)

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina

The Estimation Result of Likelihood Test Ratio Between FEM and PLS Method

Prob. Cross-section F

Effects Test

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

region is different. The coefficient of Y=0.145-0.053X1+0.012X2+0.140X3+0.008X4 basic, standard errors and t-statistics and

analysis results are presented in Table 6. The equation shows that:

1. C constant value of 0.145 means that if Based on several methods above, then some

the farmers’ term of trade, the number of tests are used to choose which method is the most

population, and the number of medium appropriate. Chow Test or Likelihood Ratio Test is

and large companies in the manufacturing used to chose which one is more appropriate for

industries is 0, then the paddy fields conversion PLS and FEM methods. The estimation results of

will grow to 0.145%.

Chow Test or Likelihood Ratio Test using Eviews 7.0

2. b(X1) = -0.053 means that if the farmers’ term of can be seen in Table 7. The test results showed that

trade is increased by 1%, while other variables the probability for the Cross-section F is 0,9211,

remained, then the paddy fields conversion (Y) which is over 0.05 (Decision: accept Ho), Ho, in this

will be decreased by 0.053%. Sign (-) indicates case, indicates that the PLS method is better than

a negative correlation between the inverse FEM method, so it can be concluded that in 95%

or opposite of the farmers’ term of trade and confidence level, PLS method is better than the FEM

paddy fields conversion, that is, if the farmers’ method.

term of trade is high, then the paddy field Hausman test is used to find the appropriate

conversion will be low.

3. b(X2) = 0.012 means that if the variable GDP the result of Hausman test in Table 8, it shows that

method between FEM and REM method. Based on

of manufacturing industries sector increased the probability value for the Cross-section F is over

by 1%, while other variables remained, the

0.05, which indicates the condition accepts Ho. paddy fields conversion (Y) will be increased In this case, Ho is indicating the REM is better

by 0.012%. Sign (+) positive indicates the than FEM method. So that as the value of probability

same direction of the relationship between the for the Cross-section F is 0,7055, it can be concluded

GDP of manufacturing industries sector and that in 95% confidence level, REM method is better

conversion of paddy fields, that is, if the GDP than FEM method.

of manufacturing industries sector is high then Based on the result of a series of estimates

the paddy field conversion will also be high. above, it can be seen that the model equations

4. b(X3) = 0.140 means that if a variable number generated by the PLS method is better in explaining

of population increased by 1%, while other the influence of variable farmers’ term of trade,

variables remained, the paddy fields conversion number of population and the number of medium

(Y) will increase by 0.140%. Sign (+) positive and large companies in the manufacturing

indicates the same direction relationship industries of the vast paddy fields research areas.

between the number of population and the Based on estimates by PLS method, obtained the

paddy fields conversion that is, if the number of following equation:

population is high, the paddy fields conversion will also be high.

Table 8.

The Estimation Result of Hausman Test Between FEM and REM Method

Prob. Cross-section random

Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Source: Statistics Indonesia (BPS), 1995-2013 (processed)

Jurnal Bina Praja 9 (1) (2017): 1-13 8 Jurnal Bina Praja 9 (1) (2017): 1-13 8

industries of -0.016026. this means conversion (Y) will be increased by 0.008%.

not happen Multicollinearity sign (+) positive indicates the same direction

between the farmer’s term of relationship between the number of medium

trade variable and number of and large companies in the manufacturing

medium and large companies in the industries and paddy fields conversion, that is,

manufacturing industries because if the number of medium and large companies

the magnitude of the correlation in the manufacturing industries is high then

coefficient is -0.016026 less than paddy fields conversion is also high.

0.8 then we can say “pass the Multicollinearity test “.

4) Classical Assumption Test

The correlation coefficients for To be accepted as a multiple linear regression

variables GDP of manufacturing model assumption, it must meet the Classic

industries sector and a number of assumption test, which includes:

the population are -0.548850. this means not happen Multicollinearity

a) Normality Test between GDP of manufacturing Based on the test results of normality

industries sector variables and a treated with 7.0 eviews program, it

number of population. because can be obtained that the shape of the

the magnitude of the correlation histogram is distributed asymmetrically

coefficient is -0.548850 less than so that we expected residuals to be

0.8, then we can say “pass the normally distributed. Based on statistical

Multicollinearity test “. test Jargue bera statistical value of

The correlation coefficients for 1.528706 with 46.56% probability, it

GDP of manufacturing industries can be concluded that the residuals

sector variables and the number were normally distributed and pass the

of medium and large companies test for normality so that testing data is

in the manufacturing industries appropriate to proceed in the study.

are -0.005481. this means Multicollinearity does not happen

b) Multicollinearity Test between GDP of manufacturing From the data processing through the

industries sector variable and eviews 7.0 program, it can be concluded:

a number of medium and large •

The correlation coefficient for the companies in the manufacturing variable farmer term of trade and

industries because of the magnitude the GDP of manufacturing industries

of the correlation coefficient is sector amounted to -0.210224.

-0.005481 less than 0.8 then we can This means that Multicollinearity say “pass the Multicollinearity test “.

The correlation coefficients for the rate and the GDP manufacturing

does not happen between variable

number of population variables and industries because of the correlation

the number of medium and large coefficient is less than 0.8. Then we companies in the manufacturing

can say “pass the Multicollinearity industries are -0.045813. this test “.

means not happen Multicollinearity •

The correlation coefficient for between a number of population the variable of farmers’ term

variables and the number of of trade and the number of

medium and large companies in the population at 0.121284. This means manufacturing industries because

Multicollinearity does not happen the magnitude of the correlation between the term of trade variable

coefficient is l-0.045813 ess than and a population of farmers because

0.8 then we can say “pass the the magnitude of the correlation

Multicollinearity test “. coefficient is 0.121284 less than

c) Heteroskedasticity Test Multicollinearity test “.

0.8, it can be said to “pass the

Based on heteroscedasticity test •

The correlation coefficient for the through eviews 7.0 program, we can find Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina Wina Dwi Febrina

influence paddy fields conversion in is no heteroscedasticity.

Java.

The number of medium and large

d) Test Autocorrelation companies in the manufacturing Based on Autocorrelation test

industries Variable through eviews 7.0 program, we can find

Based on the estimation by eviews that the probability value Obs * R-squared

7.0 program for a number of are 0.5006. Because the probability value

medium and large companies in the Obs * R-squared are 0.5006 > 0.05 can

manufacturing industries Variable,

be concluded that in this research model the result obtained the probability passes autocorrelation test.

value (significance) = 0.1953. Thus because of the probability value is

e) Test of linearity less than 0.05 α (0.1953 < 0.05), it Based on the linearity test through

can be concluded that the number of eviews 7.0 program, we know the value

medium and large companies in the of the F probability of 0.6352. Thus, the

manufacturing industries Variable probability of 0.6352 > 0.05 so that we

did not significantly influence paddy can conclude this research model linearity

fields conversion in Java. test passes.

b)

F Test

5) Hypothesis Testing Based on the estimation by eviews

7.0 program, probability value obtained a)

t-Test for the F-statistic is equal to 0.000000. •

Farmers’ term of trade variable Thus because of the probability value

Based on the estimation by eviews (significance) is greater than α 0.05

7.0 program for farmers’ term of (0.000000 > 0.05). It can be concluded trade variable, the result obtained

that simultaneously, the farmers’ term of probability value (significance) trade, the GDP of manufacturing industries = 0.0000. Thus because of the sector, the number of population, and the probability value less than α 0.05 number of medium and large companies

(0.0000 < 0.05). It can be concluded in the manufacturing industries variables that the farmers’ term of trade

significantly influence the paddy fields variable significantly influences the conversion in Java.

paddy fields conversion in Java. •

The GDP of manufacturing industries

6) Coefficient of Determination sector Variable

Based on the estimation through the program Based on the estimation by eviews

eviews 7.0, the coefficient of determination can

be seen from the adjusted R-Squared value. The manufacturing industries sector

7.0 program for The GDP of

result for adjusted R-Squared value is 0.417101. Variable, the result obtained

This means that 41.71% of paddy fields conversion probability value (significance)

in Java is influenced by farmers’ term of trade, the = 0.0000. Thus because of the

number of population and the number of medium probability value is less than α 0.05

and large companies in the manufacturing industries (0.0000 < 0.05), it can be concluded

variables, while the remaining 58.29% is influenced that the GDP manufacturing

by other variables which are not examined in this industries variables significantly

study.

influence the paddy fields conversion Based on the results of the analysis in Java.

determined by using the PLS (Pool Ordinary Least •

The numbers of population Variable Square) method, it can be seen that together Based on the estimation by eviews

(simultaneously) the farmers’ term of trade, the

7.0 program for the number of GDP of manufacturing industries sector, the number population variable, the result

of population, and the number of medium and large obtained probability value companies in the manufacturing industries variables (significance) = 0.1146. Thus

significantly influence paddy fields conversion in because of the probability value less

Java. From the F test results, it can be seen that the than 0.05 α (0.1146 < 0.05), it can be

probability value for the adjusted R-squared value

Jurnal Bina Praja 9 (1) (2017): 1-13 10 Jurnal Bina Praja 9 (1) (2017): 1-13 10

• E > 1 = elastic

medium and large companies in the manufacturing

• E < 1 = inelastic

industries variables, while the remaining 58.29% is

• E = 1 = uniter

influenced by other variables not examined in this • E = 0 = perfectly inelastic study.

• E = ~ = perfectly elastic Based on the research, we find that the farmers’ term of trade significantly influence the

As for the influence of these variables, it can be paddy field conversion in Java is in accordance with

seen in the estimated equation below: the initial hypothesis of the study. The low term of

trade caused no incentive for farmers to continue to Y=0.145-0.053X1+0.012X2+0.140X3+0.008X4 live in their paddy field production, so they tend to convert their paddy fields (Ashari, 2003). Similarly,

From the equation, we can conclude the the GDP of manufacturing industries sector variable

significantly influenced paddy fields conversion, it elasticity of each variable. Based on a slope, the indicates that industrial activities and the size of the value of Farmers’ term of trade variable amounted

to -0.053, it can be seen that the elasticity value role of the industrial sector in the GDP stimulated

of Farmers’ term of trade variable towards paddy government priorities to shift from agriculture

to the industrial sector. For farmers, the increase fields conversion is -0.053. Based on a slope, the

value of GDP of manufacturing industries sector in industrial sector becomes one of the factors

variable amounts to 0.012, it can be seen that the to switch professions to become workers in the

elasticity value of GDP of manufacturing industries industrial sector and convert their land into other sector variable towards paddy fields conversion is uses rather than the paddy field.

Based on the estimation by eviews 7.0 program Based on elasticity value of the farmers’ term for the number of population variable, the result

obtained probability value (significance) = 0.1146. of trade variable and the GDP of manufacturing Thus because of the probability value is less than industries sector variable which is smaller than

0.05 α (0.1146 < 0.05), the number of population 1, it is indicated that the farmer’s term of trade variable did not significantly influence paddy field variable and the GDP of manufacturing industries

conversion. Based on the estimation by eviews sector variable are inelastic towards paddy fields

conversion. It showed that every 1% increase in the

7.0 program for the medium and large companies in the manufacturing industry variable, the result

farmers’ term of trade will decrease paddy fields obtained probability value (significance) = 0.1953. conversion amounted to 0.053%. Instead, each 1%

Thus, because the probability value is less than increase in the GDP of manufacturing industries sector will improve paddy fields conversion

0.05 α (0.1953 < 0.05), It can be concluded that

amounted to 0.012%.

the number of medium and large companies in the manufacturing industries variable did not

C. Government Regulations to Control

significantly influence paddy fields conversion in Java. This result shows that both variables did not

Paddy Fields Conversion

correspond to the initial hypothesis of the studies. Paddy field is a private good that is legal to

be transacted. Therefore, the efforts to limit the such as on farmers’ term of trade variable pointed

The negative slope of the variables equation

occurrence of paddy field conversion can be done out that the increase of farmers’ term of trade will

is merely control. The control should be the start cause the decrease of the paddy field conversion. In from the factors that cause the occurrence of paddy

contrast, the positive slope such as on the GDP of fields conversion, there are economic, social, and

manufacturing industries sector pointed out that the legal instruments (Ilham et al., 2005). One of the increase in GDP of manufacturing industries sector

efforts made by the government to control the caused the increase in paddy field conversions.

conversion of paddy fields is through a juridical One of the interesting properties in empirical

approach by issuing various regulations such as research model is that the slope coefficient (β) Law of the Republic of Indonesia Number 5 of 1960

measure the elasticity of Y with respect to X, or on the Basic Regulation of Agrarian Principles, Law in other words, the percentage change in Y for a

of the Republic of Indonesia Number 12 of 1992 given percentage change in X (Gujarati, 2007). In

concerning Plant Cultivation System, Law of the symbolic, if ΔY indicated the changes of paddy field Republic of Indonesia Number 26 of 2007 on Spatial conversion (Y) and ΔX indicated the changes in a Planning, Law of the Republic of Indonesia Number variable that influence paddy field conversion (X), 41 of 2009 on Sustainable Land Farming Protection, then it can be defined as a coefficient of elasticity as well as the Presidential Regulation and other

Determinants of Paddy Field Conversion in Java 1995-2013

Wina Dwi Febrina

Ha and rainfed paddy fields area amounted to 2.486 approach is not maximized in overcoming problems

Ha. This means that there is an actual technical of paddy fields conversion. Lichtenberg & Ding

irrigated paddy field amounted to 18.400 Ha that (2008) states that the biggest obstacle in the

is not included in the allocation of paddy fields protection of agricultural land is institutional aspect

designation on RTRW 2007 so that it can stimulate rather than on the physical aspects. Failure to set the

the occurrence of paddy fields conversion in those institutional aspects affect the demand and supply of

areas. Thus, based on the case above, it turns land to be converted. A juridical instrument that has

the spatial instrument that expected to provide been developed by the government have not applied

protection for paddy fields precisely triggering the clear sanctions, overlapping and not supported by

occurrence of greater paddy fields conversion. economic and social instruments (Irawan, 2008). This causes the paddy fields conversion rate is still

IV. Conclusion

quite large even venturing into paddy fields with Based on the estimation result it can be

technical irrigation potential for rice production. For example, the implementation of Law of

seen that during the year 1995-2013 paddy field conversion occurs at all provinces in Java with total

the Republic of Indonesia Number 41 of 2009 is conversion about 370.000 Ha, or about 19.000 Ha/ plagued with the overlapping rules of other laws, year, with conversion rate are about 0.57% annually. such as Law of the Republic of Indonesia Number

23 of 2014 on Regional Government, where the West Java Province experienced the largest paddy

Regional Government has authority to assign field conversion in Java throughout the years 1995-

2013 with total conversion about 228.000 Ha, or

a function area. Agriculture is only a matter of

about 12.000 Ha/year.

choice for Regency/City Government in the Law Based on the resulting equation models, can of the Republic of Indonesia Number 23 of 2014. These conditions increase the chances of ignoring be seen that the farmers’ term of trade variable and

local agricultural sector. Irawan (2008) states that GDP of manufacturing industries sector significantly

regional Government focus on the progress of their influences the paddy fields conversion in Java. Based

on the slope and the elasticity, in partial, it is known region that viewed from the economic growth and revenue (PAD), The Regional Government are trying that each 1% increase in the farmers’ term of trade

to increase investment in the non-agricultural sector will be lowered paddy fields conversion amounted

to 0.069%. Instead, each 1% increase in the GDP of as it can generate greater revenue and promote the manufacturing industries will improve paddy faster economic growth. Consequently, if the case

demands conversion of agricultural land to be used field conversion amounted to 0.287%, where the

slope value of less than 1 indicates that the farmers’ for other nonagricultural uses, the local government term of trade variable and GDP of manufacturing is less likely to consider a ban on land conversion

applicable. industries sector are inelastic towards paddy fields

conversion.

Furthermore, inconsistency between Moreover, paddy fields conversion in Java needs paddy fields conversion control objectives and

determining the extent of paddy fields in the spatial to be controlled considering the vast reduction of instrument can also trigger the occurrence of paddy

paddy fields area in Java. Efforts to control paddy fields conversion in Java can be done by reducing

fields conversion. This can be seen in the research conducted by Dewi (2010) which showed that

the factors that affect paddy fields conversion. The effort required in order to reduce the paddy fields

the Spatial Plan/RTRW as an instrument layout is conversion in Java is to fix the farmers term of trade, precisely triggered by the occurrence of paddy fields especially paddy fields farmers and do monitoring conversion. This occurs due to the incompatibility of

determining the extent of paddy fields contained in in industries developments in order not to convert a paddy fields for the industrial expansion, that is the

the RTRW with the actual area where it turns paddy fields area listed in the RTRW are smaller than the policy of increasing the purchasing power of farmers

actual paddy fields. For example, The Study which so that farmers term of trade increased. Besides comparing two RTRW in Bekasi Regency year 1999 that, it also needs policies in land conversion control and 2007 showed that in the RTRW 1999, vast areas due to the development of industrial activities such allocated for paddy fields is 66.937 hectares, while as monitoring the granting of land use, especially

paddy fields for other uses.

in RTRW 2007, the area designed for paddy fields decreased into 51.093 Ha. This means that there has However, currently, the regulations to protect

been a decline in the paddy fields area. In addition, agricultural land still have not effective enough to through spatial analysis, it is known that based on

overcome problems of paddy fields conversion into the facts, the actual paddy fields area is larger than another use. Overlapping rules, rules that have not

supported by economic and social instruments,

Jurnal Bina Praja 9 (1) (2017): 1-13 12 Jurnal Bina Praja 9 (1) (2017): 1-13 12

Retrieved from http://pse.litbang.pertanian. paddy fields in the spatial instrument still being

go.id/ind/index.php/publikasi/forum-agro- obstacle in order to control paddy fields conversion

ekonomi/312-forum-agro-ekonomi-vol26- in Java and in fact, precisely trigger the occurrence

no02-2008/2084-meningkatkan-efektifitas- of greater paddy fields conversion.

kebijakan-konversi-lahan Irawan, B., Purwoto, A., Dabukke, F. B. M., &

Acknowledgement Trijono, D. (2012). Studi Kebijakan Akselerasi

First of all, praise to Allah, SWT for his mercy Pertumbuhan Produksi Padi di Luar Pulau

Jawa. Retrieved from http://pse.litbang. and guidance in giving me full strength to complete pertanian.go.id/ind/index.php/laporan- this Paper. A lot of thanks to my tutors Prof. Dr. Ir. DS Priyarsono and Prof. Dr. Ir. Noer Azam Achsani hasil-penelitian/2530-laporan-hasil-

penelitian-2012

from Bogor Agricultural University (IPB), for their Isa, I. (2006). Strategi Pengendalian Alih Fungsi support, research assistant and valuable comments Lahan Pertanian. In A. Dariah, N. L. Nurida, to revise the Paper. Great appreciation to the Irawan, E. Husen, & F. Agus (Eds.), Prosiding Research and Development Centre of Ministry Seminar Multifungsi dan Revitalisasi Pertanian of Agrarian and Spatial Planning for giving me (pp. 1–16). Jakarta: Indonesian Agency for permission and supports during conducting the Agricultural Research & Development, Ministry paper. Also Special thanks to my families for the of Agriculture; Ministry of Agriculture, Forestry extraordinary supports. and Fisheries, Japan; ASEAN Secretariat.

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