Volume 1, Issue 4, September 2020

E-ISSN : 2721-303X, P-ISSN : 2721-3021

DYNAMIC SUPPLY RESPONSE : IMPLICATIONS FOR INDONESIA
SOYBEAN CROP
Edison1, Dharia Renate2, Denny Denmar3
1
University of Jambi, Indonesia.
2
University of Jambi, Indonesia.
3
University of Jambi, Indonesia.
ARTICLE INFORMATION
Received: 13 July 2020
Revised: 15 August 2020
Issued: 24 September 2020
Corresponding author: first
author
E-mail:
ediedison950@yahoo.co.id
dhariarenate@yahoo.com
dennydenmar101264@gmail.c
om

DOI:10.38035/DIJEFA

Abstract: The purpose of the study is to explore the
supply response model of the soybean crop in terms of
alternative specifications also implications economics.
To apply model, considerations through the availability
of production lags concept also the existence of
expected price and gross revenue because of producer’s
response explanatory preference on movement
economic situation. The results showed that the existing
lags were due mostly to the problems also quick
adjustment expenditure rather than correcting expected
time. The quantitative result was the same as gross
margins and prices alternative specification as the
availability of economic decisions. Meanwhile,
elasticities price found through the model response
specification tended around a fourth of the model
applying price specification. The model response
specification produced more explanation in terms of
production also elasticity of input expenditure.
Keywords: Dynamic Supply response, Production
response, and Soybean crop

INTRODUCTION
The agricultural sector has to play a crucial role in Indonesia's economic development,
where primarily exists an important change in food self- sufficiency also improving food
markets nowadays. Efforts toward diversifying economic base through agriculture, increased
revenue through money valuating also supporting to nation’s gross domestic product has to
evaluate a balanced food supply response characters through the price policy program also
another non-price component of food yield. Considering of agricultural supply response on
view policy also market characteristics and also planting seasonal is not quickly, but
continuous a time-lapse called lag (Gujarati and Porter, 2009). Such lags offer enough time
for decisions that accentuate food accessibility and food security (Tollens, 1998). The past
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food policy framework in Indonesia failed to tackle food insecurity because it mainly focused
on agricultural productivity improvement such as the correct meaning of food selfsufficiency, employment era, foreign value earnings also poverty reduction (Bappeda, 2018;
Edison, 2020). No empirical study has linked the productivity growth of soybean to settle
accessibility such considering its real output longtime especially deals with agricultural
supply response.
Soybean (Glycine max) supply will assume steady accessibility if and only if the
planned (expected) supply, from a planned demand required for healthy living, meets the
actual supply over some time. Incessant shortages or surpluses arising from reallocation of
both price and non-price factors invoke supply instabilities and deviation from the planned
supply. Soybean supply in Indonesia is unstable. There is a domestic annual supply shortfall
of about 100,000 tons for soybean meal and 300,000 tons for vegetable oil. The supply
increased by only 2,600 tones/farmer in the late ’60s but dropped by 18.6% following farm
resource reallocation along the oil boom season in the ’70s (Tripath, 2008). It rose again by
9.11% annually from 1984 to 160,000 metric tons in 1995 and later to 550,000 metric tons
in 2014, leaving a national demand gap of 200,000 metric tons that must be met through
importation.
Agriculture sector still plays a crucial role in Indonesian development such as
supporting to GDP, money value-added also the labor sector. The agriculture sector had
almost forgotten until 1986’s since development was considered to be the same as the
industrialization sector. This statement was supported by the thing that the industry sector is
the crucial sector, while the agriculture sector is the usual sector also does not have an impact
on incentives. This argument moves to explore the agriculture sector by moving domestic
trade versus agriculture. The statement that the agriculture sector is not a crucial sector
meaning that resources came from agriculture could be moving to another sector of the
economy sector without affecting agricultural development significantly (Pearson et al, 1991;
Edison, 2014).
Nerlove in Edison (2020) noted that producers’ response to variations in lagged
agriculture prices also input prices in regional and international food markets both in simple
and sophisticated results in a food supply dynamics. The unsteady food supply is the reason
for unstable food fluctuations and food insecurity in Indonesia. Food is secured when there
is adequate access to food at all times. At the household level, adequate access to food occurs
when food is not only available but affordable for healthy living at all times. Food security
is thus defined as the actual output of soybean that meets a planned supply needed for healthy
living. Consideration of right supporting such as price or non-price support is mostly to have
an impact on best supply responsiveness, on the other hand, that has disincentives impact.
Importantly, movement in own-food prices gets more benefit as producers are mostly to have
yielded more of that condition at good prices, so it can be better off on long-term food security
(Oktaviani and Asmarantaka, 2010). BPS (2018) studied that it is important to care problem
in prices of food that is responsible for problems in real and yield supply expectation of food
in economies mostly. Arranging in input and prices may decrease soybean yield and income,
continuing to prepare a decrease in real output from output expectation. It can direct to a
selection of an alternative business by smallholder producers among the next planting
season.
According to Mose and Kuvyenhoven (2007) about modernizing agricultural traditionally
argued that lack of ability to agricultural contribution to development has created some doctrine also
political problems. The problem with that doctrines was producers have less response to economic
values. A crucial problem was that does agriculture yield in bad producers’ income usually move up

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through the best allocation of input in their agricultural sector? How can value-added on increasing
agricultural yield be got by using the allocative efficiency of farming properly? (Guyomard et al,
2003).
Therefore incentive factors which have played an important role in succeeding in any
agricultural improvement program nowadays usually happened. Some empirical researches in the
year of 1960s (Behrman 1989, Choi and Helmerger 1993, Keeney, and Hertel. 2008; Edison, 2011)
expressed a problem on producers reaction on economical stimuli also response on the allocation of
input using time series also cross-sectional data analysis in some underdeveloped places.
The commodity of agriculture in Indonesia along fifty years or so can be seen as some
improvement in the input market also output market then experienced some policy movement causing
in some improvement in the condition of market incentives got by producers. Meanwhile, very small
improvements had been commodity-specific/commodity-oriented, such there has been a lot of
combination movement in the incentive sector (Edison, 2011).
Picture of soybean product has a crucial view on improvement in increasing agriculture
contribution also the capacity expectation and industrial sector development which influenced by
supply on agricultural input. From that point, it seems that there is some contribution of the soybean
sector with industry also another sector of economic value, which creates labor value also valueadded in the economic sector (Yu et al. 2010). Research aims were to improve our knowledge of
response specification also a prediction of supply response on agriculture than to create a tool of
policy analysis on agriculture. The major issue of this study is, what level of adjustments is required
by actual soybean supply to close up the planned supply needed for healthy living over time? Again,
what speed of adjustment is needed to establish a long-run equilibrium for steady-state food security?
The issues have raised price oscillations and dynamism in equilibrium supply that initiated food
shortages or surpluses and food insecurity (Edison and Wahyuni, 2020). The study is aimed at
determining the level and speed of adjustments needed for actual soybean supply to meet the planned
supply and arrest food insecurity in Indonesia, given the present input and commodity price policies
in a changing climatic condition.

LITERATURE REVIEW
To grow food security, the agricultural commodity has played a crucial thing in
supporting the food commodity sub-sector, also has an impact on economic development to
stabilize the market condition. Food security is successful if humans generally get real also
business access to enough, good, and hygiene food to fulfill their healthy food and
preferences for a healthy life (FAO, 2006). Tollens (1998) explored three aspects of food
security at household categories. They are; the existence of food, which states which food is
sufficient for a healthy life. Food is available when there is enough production and supply is
stable over time. Second is access and food utilization, prefer also enough and the best food
for staying in good health. Accessibility food is accounted for by its market unit prices and
cost of the food and value addition to the food. Finally, food must be affordable, implying
that there should be enough household income to ensure adequate procurement of food that
will sustain body requirements for an active and healthy life.
At the national and international levels, food security has meaning enough world food
existence also enough power to fulfill food need words to receive or send, accept good food
coming from countries of surplus food (Edison, 2020). In terms of delivery operation of “food
security” is on linkage into micro also macroeconomics aspects. Edison (2014) stated the
food security sector is the linkage point of macroeconomics also the regional part of the food
market sector even-though it appeared that international agricultural impact on the food
sector movement. This condition was drawn on individuals also aggregate food sector
linkage, like the regional food sector will be linkage micro markets at the regional level.
Development of agriculture and rural is connected to market stability, which actives when
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producers respond to enough production factors also food price policies that expressed the
best food supply system, therefore the best food secured regime occurred. Meanwhile,
Indonesia’s local production of soybeans is on the upward regime but has continued to have
a rapidly growing demand deficit and increasing market price since 2007. Indonesia
Development Board (Bappenas, 2018) stated that increased lagged price conditions had bad
conditions than increasing import also bad value-added regime, which changes to other food
insecurity policies.
Function on the supply sector explains how much total production used for sale varies
as its price varies to other prices of products (Cochrane, 1995; Nainggolan et al. 2018).
Cochrane explores on supply response equations. Supply equation explains the quantity that
would be supplied at changing prices with all other things constant, while the supply response
relationship describes what will happen to the quantity supplied when all other things are not
held constant (Askari and Cummings, 1977; Edison and Siata, 2017). Nerlove in Edison
(2020) explored most of the operational framework in supply response articles.
Supposed supply equation is explained as
Yt
=
a0
+a1
Pt
+
a2
Zt
………………………………………………….………………... (1)
Yt = Actual quantity produced
Pt = Actual price of soybean produced.
Zt = Supply Shifters
Yt = a0 + a1 P*t + a2 Z*t…………………………………………………………………
(2)
Where:
Yt = Quantity produced in time t
P*t = Expected price
Z*t = Supply shifters expected
The expected price is not observable and is explained as expected „normal‟ price, i.e.,
the level about which the future price is expected to fluctuate. This can be expected
as:
Actual P-Pt-1
P* - P*t-1 = β (Pt-1 - P*t-1),
0 ≤ β ≤
1………………………………………………….. (3)
We assume the expected price is the actual price. P = P*
We can get the following equation by getting the value of P* from equation (2)
and substituting in into equation (1) and rearranging it,
Yt = b0 + b1 Pt –1 + b2 Yt-1 + b3 Zt– 1 + b4 Zt-1…….
…………………………………..… (4)
The equation (3) can be estimated economically.
To estimate elasticities the formula used was ∂Y/P. P/Y the first term for the short
and long run will be: Short-run ∂Yt/Pt-1 and Long run: b1/1-b2
The planned or the desired (expected) output supply of soybean (Yd) in a year(t)
is a distributed lagged model that is explicitly expressed in Nerlovian sense as:
Yd = þO + þ1Pt–1 + þ2ExpPt–1 + þ3EXPvol+ þ4Rt + þ5 At + st
……………………….…… (5)
Where;
Yd
= the desired (expected) quantity of soybean supplied in kilogram in a
year(t)Pt-1 is expected unit domestic price in a year(t) based on the previous
expectations measured in naira
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ExpPt-1 = the expected export price of soybean-based on previous expectations in
naira
EXPvol = the volume of export in a ton
At
= the average area of land with soybean cultivation in a year(t), measured in
hectares
Rt
= the volume of rainfall in a year(t) measured in millimeters
þ′s
= vectors to be estimated in the equation.
Nerlovian model in equation (5) explores the expected price also appropriate factors of
yield supply of soybean. This model is crucial in movement along with real and expected
level yield supply. The movement was because of producers' response to the price level of
production factor, policy consideration, response to yield price, production and value risk,
season movement also variations in acreage factors. Meanwhile, all movements are explored
in the Nerlovian adaptive expectations model because of yield movement along with
beginning in proportion to the gap between real output supply in the current period and real
output supply in the previous period which can generate dynamical food insecurity problem
if best supply adjustment is got in the long-run. Therefore, a distributed lagged model creates
an autoregressive (dynamic) model (Edison, 2020). The Nerlove adjustment model for
soybean supply is thus presented as:
Yt
−
Yt–1
=
ð{Yd
−
Yt–1}
…………………………………………….…………………. (6)
Note: Yt is real supply in a year(t), Yt–1 is previous output supply, Yt − Yt–1 translate
to change in yield supply of soybean, and Yd is already stated in equation (5). The model
substitution will show real yield supply in a year(t) as
Yt − Yt–1 = ð{þO + þ1Pt–1 + þ2ExpPt–1+ þ3Expvol + þ4Rt + þ5 At + St − Yt–1}
………….. (7)
Then equation (7) can be explored more as
Yt = ðþO + ðþ1Pt–1 + ðþ2ExpPt–1 + ðþ3Expvol + ðþ4Rt + ðþ5 At + (1 − ð)Yt–
1 + ðSt ……..(8)
The real yield supply of soybean in the Nerlovian model covers producers' response to
some components like the season, acreage under soybean land, beginner supply also expected
price from previous experience.
RESEARCH METHOD
Analytical Procedure and Model Specification
Trends in agricultural supply follow food real local supply as a proxy. This is described
using relative mean values and ratios. The ratio of a change in mean supply relative to the
previous mean supply multiplied by 100 gives the percentage in mean supply. Again, the
ratio of soybean supply to total agricultural supply represents the soybean supply index. Time
series data from 1986 to 2019 was chosen for this research. The problems considered in food
supply response are preferred expectation price adjustment or partial yield adjustment as
explored in a model (5 – 7) above. The gap between the planned and actual soybean supply
in Indonesia created a time-lapse that can be explained by the Nerlove adjustment model
(Edison and Siata, 2017).
Model (8) is suitable in the Cobb-Douglas Function to find a technical relationship.
The use of the Cobb-Douglas function has found supply elasticities because the parameter
estimates of the Cobb-Douglas function are elasticities. Then, it is hoped a priori that all
variables should have a direct effect on soybean yield supply response within the period the
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study reviewed. Therefore the following parameters and their representatives were possible
from equation (5):
ð þO = A, ð þ1 = B, ð þ2 = C, ð þ3 = D, ð þ4 = E, ð þ5 = F and (1 − ð) = G
If (1 − ð) = G then ð = 1 − G. The final Nerlove estimation is:
lnYt = lnA + BlnPt–1 + ClnExpt–1 + DlnExpvol + ElnRt + FlnAt + GlnYt–1 +
ð St ……….(9)
The short supply elasticity could be generated directly from parameter (B) in equation
5.0, while the long-run adjustment in the elasticity of supply as used by Edison and Wahyuni,
(2020) is expressed as:
Longrun Supply Elasticity{LREq} Short-run Supply Elasticity H0 as against H1 ∶ n <
0 and it is based on the t- statistics of the coefficient n from an OLS estimation of equation
7.0 above. This is followed by the cointegration modeling. Cointegration is a concept for
modeling equilibrium or long-run relations of economic variables (Dickey and Fuller, 1981).
Establishing a long-run supply response of soybean is built around the investigation of the
number of cointegration relations, estimate their parameters, and place appropriate
restrictions. This is finally followed by designing the complete model, assessing the quality
of the model by adjusting the error mechanism for further investigation, and or forecasting.
RESULTS AND DISCUSSION
The unit root test on the series
Table 1 is the unit root test of all the series included in the model. The Augmented
Dickey-Fuller (unit root) test was performed on the series as shown in Table 3. The
stationarity test on the series was done at their level 1(0) and first difference 1(1) with their
corresponding leg lengths in parenthesis. The null hypothesis of no presence of unit root
cannot be rejected for the series in their level 1(0) as shown in Table 3 as the ADF of all the
series were lower than the critical value of (q − 0.01) -3.558 but were higher than the value
when differenced by 1(1). This means that the alternative hypothesis is accepted that the
series are stationary at first difference 11(1). The unit expected domestic prices, export price,
the volume of export, present mean rainfall, and area under soybean cultivation each had a
unit root at 1(1) see Table 1. It could be deduced from this finding that there could be a shortrun divergence and long-run relationship with the series, given the non- stationary properties
at their levels and the lag length after differencing. The implication of non- stationarity or
random-walk behavior in the variables highlighted stochastic trend behavior. Hence, a
spurious estimation is imminent if a technical relationship is carried out at their level 1(0)
with the series. Such stochastic error generated in the series is invariant with time (Edison,
2020). Therefore, error correction representation of the relationship is very imminent.
Testing for co-integrating vectors
It could also be seen from the result in Table 2 that the Trace statistics performed better
than the Max-Eigen statistics at (q = 0.05). There is evidence of non-stationarity of the series
due to their random walk behavior of unit root. A further test of co-integration with the Trace
statistics is imperative. The cointegrating modeling brought the equations back to stationarity
through a linear transformation [8]. The Max-Eigen value in Table 2. showed no
cointegrating vector at (q = 0.05) but the Trace Statistics indicated 2 cointegrating.
0.45
0.35
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Soybean Supply index

Volume 1, Issue 4, September 2020

1986

1990

1994

1999

2003

2007

2011

2015

2019

Fig. 2. Trend in Soybean Contribution to Total Agricultural GDP from 1986 to
2019

Table 1. Stationarity (Unit root) test
Variable

At level form

Export price

ADF(lag
length)
-1.7622
(2)
-0.7475(3)

Export volume

-1.6476(1)

Mean rainfall

-2.3012(2)

Mean acreage

-1.7444
(2)
-0.5425(2)

Domestic price

Previous output

Remark
Non
Stationary
Non
Stationary
Non
stationary
Non
Stationary
Non
Stationary
Non
Stationary

At 1st
ADF(lag
lenght)
-7.1844(1)
-8.7927(2)
-9.8326(1)
-5.9564(2)
-6.3672(1)
-5.2645(2)

Rema
rk
Statio
nary
Statio
nary
Statio
nary
Statio
nary
Statio
nary
Statio
nary

difference
Source: Result print out from E-view software, 2012. (q = 0.01) are -3.558 and -3.592
vector (equation) at q = 0.05. The Trace statistics of 79.84 is greater than the
critical value (q = 0.05) of 69.82 for at least two (2) cointegrating equations. The
existence of at least one co-integrating equation/vector as shown by the Trace
statistics indicated that there is an evidence of a long-run convergence or
equilibrium despite the divergence of the series in the short-run (Greene, 2003).
Determinants of Soybean Supply and Error Correction Mechanism (ECM)
The maximum likelihood estimation of equation 5 revealed the determinant of

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soybean output supply in Nigeria. The result showed the effect of the included variables
on soybean output supply and other functional parameters. The log likelihood ratio, Coefficient of Multiple Determinant (CMD) and Durbin-Watson statistic are 11.79, 0.229
and 1.846 respectively. It could be deduced from this finding that 22.9% variations in
soybean output supply could be explained by the variations in the included explanatory
variables such as the expected domestic prices, export prices, volume of export etc.
The model does not have autocorrelation with a Durbin-Watson value of 1.85. An error
correction vector (ECM) of - 0.344, which is negative and statistically significant at q =
0.05 was obtained. The ECM of this model indicates that more than 34.4% adjustment
towards long-run equilibrium for soybean output supplies is completed in one period.
The speed of the short-run divergence, converges to equilibrium in the long-run with
about 34.4% proportion of the disequilibrium in the soybean supply in less than one
period being corrected in the next period.
Furthermore, it is revealed in this result that all the variables have direct effect
on farmers output supply response except lagged export volume. This implies that apart
from expected volume of export, other variables showed consistent behavior with a
priori expectation. The result show that the lagged value of yield has about 0.11%
increase on the current output supply of soybean, implying that actual yield in the
present year is sluggishly responding positively to last year’s yield in the economy.
Though the food self-sufficiency policy regime of the country has direct effect on supply
responsiveness as farmers adjust with output supply to increase their supply following
what happened in their previous supply, it may drag too long to meet the planned supply.
The issue of meeting the demand deficit of 36.3% as noted by (BPS, 2018) due to
sluggish inelastic supply nature of this product can simply be averted by massive support
in production by the small holder farmers in Indonesia. Similarly, in unit lagged
domestic price of soybean by 1.0% changing will create to move up 0.01% response in
yield supplied. This represents that supply responsiveness is mainly affected on best
domestic price expectation, therefore, food security with respect to soybean supply
responsiveness is on best domestic price expectation. The same finding was got using
expected export prices. There is a main best supply response of 0.028% with move up
lagged export price compare to a unit increase in export volume. An increase in lagged
export volume will force down soybean supply response by 0.022%.
lnQt= 0.042 + 0.010lnPt-1+ 0.028 ln Expt-1 - 0.022 ln Expvol + 0.093lnRt +
0.018lnAt + 0.112lnQt
(0.018)∗∗ (0.004)∗∗. (0.019)
(0.011)∗∗.
(0.013)∗∗∗. (0.022)
(0.036)∗∗∗

ECM
= −0.344∗∗∗:
Log likeliℎood ratio = −11.79:
= 0.230:
R2
= 0.201:
Adj R2
Durbin − Watson
= 1.846
The study showed best explanation in price policies on soybean productivity in
Indonesia. Soybean price policy era in Indonesia tends to support productivity increasing
component obeying on international market movement mainly on external demand and
domestic demand. The contrary relationship of export volume also inelastic nature
soybean supply with the planned supply may be the reasons for the increased market
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prices. There may have been a lower than proportionate increase in expected
output. Soybean farmers strived for increased food security with ignorance of inelastic
supply nature and prices on their revenue. Policy era which explored farmers’ resources
use has to be done with caution so that output will expand massively at a lower input
cost. Hence a look at input efficiency is suggested in this study. On the contrary, to get
a good performance has to be way of best international market. Because price affects
supply, treatment has to be create at regional yield stage to link targeted global demand
also standard at a bigger price. Moreover, the average rainfall also expected place under
soybean plantation will move up real supply of soybean. Even-though, this opposite with
Edison and Wahyuni (2020) result, extended growth of soybean in the village (with short
rainfall duration) needs an other source of water like irrigation to diverse its production
to other place also maintain another season production.
Elasticity, Soybean Supply Response and Food Security Prediction
From estimation, it found that coefficient of adjustment was 0.794. It has
meaning that positive value in short run estimation indicated its expected yield supply
tended to positive. So that increasing in real supply was never coming quickly however,
producers were usually active to make an expected output supply to real supply value in
short-run adjustments.
In the very short-run, however, this situation seemed that expected price on
domestic also export were all inelastic. Every changing expected price on domestic also
export by 1.0% has impact on increasing proportionately real output of 0.017% and
0.034% respectively. This results explained evaluation that Indonesia regional yield of
soybeans was staying in upward trend but still did not connect quickly improving
demand. This increased food insecurity status as this demand deficit has affected price
of soybean meal to double in 2012. These condition of high prices had big impacted on
increasing import of soybean yield. The same effect is observed on rainfall and acreage
with 0.089% and 0.024% increase in actual output respectively in the short-run. It is only
export volume that is negative but still inelastic.
Table 2. Unrestricted co-integration rank test (Trace and maximum-eigenvalues)
Hypothesi
zed
(No. Of
CE(s))

Eigenval
ue

Trace
statis
tics

None*

0.5487

At Most
1*
At Most
2*
At Most 3
At Most 4
At Most 5

0.5273

114.0
6**
79.84
**
47.62

0.5157
0.2155
0.1305
9.33X108

16.65
6.01
4.01X
10-6

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Critic
al
Value
(q =
0.05)
95.75

MaxEigen
statisti
cs

Critical
value
(q =
0.05)

34.22

40.08

69.82

32.22

33.88

47.86

31.18

27.58

29.80
15.49
3.84

10.43
6.01
4.01X1
0-6

21.13
14.26
3.84

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Table 3. Short and long-run elasticity of soybean supply in Indonesia
Variables

Short-run
elasticities

Co-efficient of
adjustment

Longrun
elasti
cities
0.011

Expected domestic
0.017
price
Expected export price
0.034
0.032
Expected volume
-0.036
-0.025
exported
Rainfall
0.089
0.794
0.106
Acreage
0.024
0.020
Previous output supply
0.112
0.126
From estimation, it just few improvement explored using all parameters inelastic
in long-run. This result was consistent as (Edison and Wahyuni, 2020). Characteristics
in short-run had contribution in long-run also revision was hoped with producers model
of adjustment. Therefore, better proportional improvement in real product is going to
make producers react to price food policy era.
Table 4. Structural co-efficient, their significance and value of R2 for soybean
production response in Indonesia
Variable

Coefficie
nt
6,82

Standar
d Error

0.372

0.067

YRt-1

0.608

0.146

Dt

- 0.319

0.112

R2
R2 (Adjusted)

0.9167
0.8792

Consta
nt
PRt-1

tRati
o

Significan
ce

5.55
2
4.16
4
2.84
8

***

0.867

***
**

*** = Significant at 1 percent level of Significance.
** = Significant at 5 percent level of Significance
Evaluation determination co-efficient determination on yield response model showed
that 88% dynamic Indonesia soybean yield is presented by internal factors existed in
equation. Soybean lagged price coefficient had positive value with coefficient of 0.372. The
value was significant at 1% also showed that when soybean price changed one unit in last
year, it has impact on production of 0.372 units. Value and magnitude of coefficient got as
expected. Then coefficient of production factor had a positive value with a number of 0.608
and was significant at  1% also that explained lag production showed a good impact on
soybean yield. Magnitude and value of coefficient was as expected. Furthermore, dummy
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factor showed government era before in 1998. Value coefficient showed negative, like it was
hoped and number of 0,319 also was significance at  5%. Negative effect of government
era in yield sector seemed because of non-existing inputs at important step in production
process.
CONCLUSIONS
There is very important exploring food need by minimizing gap between planned supply
and actual quantity supplied of soybean. It is proof of short-run dynamic supply inside also
outside price inelasticity in which supported in the long run. So that, self sufficiency framework
the present transformation agenda of the federal government should not target only productivity
growth but also an elastic actual output in an efficient input use. This will contribute better
proportion improvement in soybean response needed to explore food in Indonesia.
The study therefore, stated an elastic soybean output at reduced market prices. It is
suggested that an efficient and cost-effective input use as well as expanded acreage with
irrigation (that will contribute quick rainfall season) was inserted in policy model in
transformation agender of the federal government. It will initiate an output response that
invigorates both domestic and export supply at a reduced domestic price. Expensive local price
can cause down export also it requires immediate improvement. Such improvement comes if it
was investigation deeply on input use efficiency. A close look at input efficiency will suggest
the cost effective production system that can increase output at a reduced cost, hence reduced
market prices. Again, useful of input use efficiently will improve producers benefits also output
so that farmers will have more incentive to produce and export will be increasing. Other use of
soybean also value added to yield can create better unit price, that can change to improved yield,
benefits also then supply yield response.

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