ISSN: 2407-814X (p); 2527-9238 (e)
AGRARIS: Journal of Agribusiness
and Rural Development Research
Vol. 6 No. 2 July-December 2020
Article history:
Submitted : November 16th, 2019
Accepted : November 3rd, 2020
Muhammad Joni Iskandar*, Jamhari
Universitas Gadjah Mada, Indonesia
*) Correspondence email: joniiskandar1508@gmail.com
Efficiency of Rice Farming in the Corporate Farming Model in
Central Java
DOI: https://doi.org/10.18196/agr.6297
ABSTRACT
The corporate farming approach on rice is aimed to increase the production on
fields with the constraints of limited land area. The present research was aimed
to analyze the impact of the implementation of corporate farming on the
production, efficiency and inefficiency of the law land farming. The study was
determined purposively in farmers’ group union “Tani Mandiri Dalangan” in
Sukoharjo Regency, Central Java. Sampling of the respondents used a census
method involving all members totaling 51 farmers over two growing seasons. Data
were analyzed using the frontier stochastic production function Maximum
Likelihood Estimation (MLE) method frontier computing program 4.1c. The
results showed that the corporate farming of rice production was affected by land
area, Urea fertilizer and NPK fertilizer. Technical, economical and allocative
efficiency corporate farming of rice farming has not been efficient. Technical
efficiency shows the lowest efficiency value due to the use of factors of production
exceeding the recommended regional dosage. Socio-economic factors that
significantly affected the technical inefficiency of the low land rice farming are
education, family size and extension.
Keywords: Corporate Farming, Efficiency, Inefficiency, Production
INTRODUCTION
Agricultural land is the type of land that has been converted the most, especially paddy
fields. Land conversion to industrial, service, property and residential sectors causes a
transformation of the structure of agricultural land tenure. This is because the opportunity to
increase land area is very limited and the proportion of farmers' land ownership is getting
narrower (Susilowati & Maulana, 2016). The average land ownership for farmers nationally
is less than 0.5 ha (Subejo, 2018). The scale of land tenure is low and tends to be unstable,
which affects the ability of farmers to produce. Therefore, farming profits are relatively small.
Even if there is an increase, the profits obtained will show a meaningless increase due to high
production costs (Suharyanto, Mulyo, Darwanto, &Widodo, 2015).
Land availability as agricultural input is an absolute prerequisite for realizing a
sustainable agricultural sector. Land plays an important role in line with regional
development, population growth, and economy, while land area is fixed in nature, in the sense
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that the total land resources of an area are fixed (Zakaria & Rachman, 2013). The scarcity of
land resources is not only a limited supply but also fragmentation that is difficult to avoid due
to the system of inheritance culture that develops among farmers. Land fragmentation is an
obstacle to agricultural development (Asiama, Bennett, & Zevenbergen, 2017). Modernization
and mechanization of agriculture are difficult to do on narrow and separate lands (Liu et al.,
2019). In addition, fragmentation causes a decrease in the production of productive land so
that the costs sacrificed by farmers to achieve potential production are high (Munnangi,
Lohani, & Misra, 2020).
Efforts to control land-use change and fragmentation can be carried out by
implementing corporate farming. Corporate farming is an amalgamation of paddy fields to be
managed jointly under the auspices of one managerial (Musthofa & Kurnia, 2018). In this
case, the farmer becomes a shareholder in accordance with the area of land they own so that
the distribution of results is based on land area. Meanwhile, individual agriculture is managed
personally. Thus, intensification through corporate farming is a strategic step because
increasing production through extensification is not possible, especially the high
fragmentation and conversion of land to non-agricultural functions. In addition, farming costs
can be minimized by mechanization, although agricultural labor is still available, in limited
numbers (scarce) and the wages are quite high, while farmers are faced with limited capital.
The corporate farming model is more efficient than traditional farming (Kostov, Davidova, &
Bailey (2019). However, the implementation of corporate farming raises various new
problems, including farmers who apply their land, as a corporate will lose their jobs because
they do not have other alternative jobs that provide better welfare. The existence of socio-
cultural differences causes some farmers not to fully entrust their land to be managed as a
corporate and the distribution of results is not appropriate because the profit- sharing is
determined based on a land area without considering land fertility (Musthofa & Kurnia,
2018).
The center for low land rice development as well as the implementation of corporate
farming is located in Sukoharjo Regency. The rate of increase in production and productivity
of low land rice in Sukoharjo Regency during the period 2010 to 2016 fluctuated with an
average production of 314,753 tons/ha and productivity of 65.46 kW / ha (BPS, 2018). The
highest increase in production in 2016 was 391,675 tons/ha, but productivity tended to
decrease by -3.18 kW / ha or -0.62%. Slowing productivity is partly due to inefficient
managerial use of production factors. Even though the use of inputs is the key to the success
of rice farming (Yuliana, Ekowati, & Handayani, 2017). Therefore, improving production
through efficiency is the right choice. Through efficiency, farmers can determine the number
and combination of production factors used to produce farming efficiency.
Previous efficiency research has focused more on the aspects of individual agriculture
that are managed independently. Among those researches are the estimation of the efficiency
of rice farming in Central Java using frontier analysis (Darwanto, 2010), analysis of the
efficiency of rice farming in several rice production centers in Indonesia using the stochastic
frontier estimation (Kusnadi, Tinaprilla, Susilowati, & Purwoto, 2011), technical efficiency
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research , economic and allocative rice farming in Central Lampung Regency, the estimation
of the Cobb-Douglas stochastic frontier production function using direct farm input variables
(Yoko, Syaukat, &Fariyanti, 2014), analysis of efficiency and income of rice farming in
SengahTemila District, Landak Regency using the stochastic production function frontier
analysis (SFA) (Pudaka & Prasetyo, 2018), research on production analysis and efficiency of
integrated rice crop management in Bali Province by comparing the application of the SL-PTT
system using the stochastic function production maximum likelihood estimation (MLE)
(Suharyanto, Mulyo, Darwanto, & Widodo, 2015).
Meanwhile, the literature on the efficiency of the corporate farming model of rice
farming is still rare. Therefore, research on the efficiency of rice farming in the corporate
farming model needs to be done. This study aims to estimate the efficiency of rice farming
using the corporate farming model in Sukoharjo Regency technically, economically, and
allocatively. The application of rice farming with the corporate farming model tends to make
farmers more careful in managerial use of production factors to achieve minimum cost
efficiency, especially in the use of labor input which until now has experienced scarcity, and
ultimately the labor wage is getting higher in production. It is hoped that the results of this
research can be used by farmers as a consideration for the sustainability of agriculture in the
corporate farming model as an anticipation model for land-use change and increasingly
massive fragmentation.
RESEARCH METHOD
The research location was determined purposively, namely Sukoharjo Regency,
Central Java, with the consideration that it is a center for low land rice production as well as
a national food barn that implements corporate farming in 2017. The sample is limited to
members of farmers’ group union “Tani Mandiri Dalangan”. The samples were taken using a
census technique for each farmer group in this farmers’ group union, including 7 farmers for
the “Ngudi Rahayu” Farmer Group, 16 farmers for the “Asri Rata” Farmer Group, 13 farmers
for the “Ngudi Rejeki” Farmer Group, and 15 farmers for the “Ngudi Mulyo” Farmer Group.
The total sample as a whole is 51 samples of farmers. The data structure for measuring
production factors and efficiency includes low land rice farming input-outputs collected for
two growing seasons in order to obtain inter-season variation in production. Data were
collected through structured interviews with all members of corporate farming.
The data is estimated using the stochastic production function frontier, computation
program frontier 4.1c. This model is used based on the consideration that corporate farming
farmers have reached their limit of production (frontier) so that on the same land, production
can still be increased by using proportional input and existing technology. Production
function parameters estimation, technical efficiency, and source of inefficiency are estimated
simultaneously by using the technical efficiency effects model with the Maximum Likelihood
Estimation (MLE) method. In addition, the source of inefficiency can be estimated whether
the dominant term error occurs due to inefficiency (μi) or noise (vi) such as weather, pests and
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157 Efficiency of Rice Farming ….. (Iskandar et al)
diseases, natural disasters, and so on. Furthermore, economic and allocative efficiency is
estimated by deriving the dual cost function from the production function.
The empirical model of the Cobb-Douglass frontier stochastic production function
which was used to estimate the production function includes rice production (Ln Y) (kg),
intercept (βo), estimator parameters (β1-β6), random/noise variables (vi), technical
inefficiency effects. (μi), and X1-X6 are the quantity of conventional input from farming which
consists of land area (ha), Urea fertilizer (kg), SP-36 fertilizer (kg), NPK fertilizer (kg), pesticides
(liters), and labor. (HOK). The model can be written as follows.
Ln Y= β0 + β1 ln X1 + β2 ln X2 + β3 ln X3. + β4 ln X4 + β5 ln X5 + β6 ln X6 + (vi -
ui).................................................................................................................................(1)
The expected value of all estimating parameters β1-β6> 0 is positive, which means that
additional use of production factors can increase rice production.
The technical efficiency of corporate farming is analyzed through the ratio of actual
production (Yi) to potential production (Yi^*) as follows (Jondrow, Lovell, Materov, &
Schmidt, 1982).
TEi=
Yi
Yi∗
=
E (Yi|Ui,Xi)
E (Yi|Ui=0,Xi)
= [exp (-μi)]...........................................................................................(2)
TEi is the technical efficiency of the i-th farmer, exp (-μi) is the expected value of
technical efficiency, namely (0 <Tei<1). Meanwhile, to determine the value of the distribution
parameter (μi), it is assumed that it is free and normally truncated with N (μiσ^2). The effect
of technical inefficiency in this study uses the following equation.
Economic efficiency is analyzed by reducing the stochastic frontier cost function
including rice production costs (C) (IDR), estimating parameters (β1-β6), random/noise
variables (vi), technical inefficiency effects (μi) and P1-P2 is the price of farm input. Including
rice production (kg), urea fertilizer price (IDR/kg), SP-36 fertilizer price (IDR/kg), NPK
fertilizer price (IDR/kg), pesticide price (IDR/lt) and labor wage-price (IDR/HOK). The
equation for the corporate farming cost function of rice is as follows.
Ln C = β0 + β1 ln P1 + β2 ln P2 + β3 ln P3 + β4 ln P4 + β5 ln P5+ β6 ln P6 + (vi- μi)
...................................................................................................................................(3)
According to Jondrow, Lovell, Materov, & Schmidt (1982) economic efficiency is
determined by the ratio of the minimum observed total cost of production (C*) to the total
actual total cost of production (C) as in the following equation.
EE=
𝐶 ∗
𝐶
=
E (Ci|Ui=0,Yi,Pi)
E (Yi|Ui,Yi,Pi)
= E [exp (- Ui | ε)]..............................................................................(4)
By using the 4.1c frontier computation program, the cost efficiency value is obtained.
Therefore, the economic efficiency of corporate farming is obtained using the following
equation.
EE=
1
Cost Efficiency (CE)
...............................................................................................................(5)
Economic efficiency is a combination of technical and allocative efficiency so that
allocative efficiency (EA) can be obtained by the following equation.
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EA =
Efisiensi Ekonomi (EE)
Efisiensi Teknis (TE)
.........................................................................................................(6)
RESULT AND DISCUSSIONS
Recently, corporate farming has not been fully implemented. Farming activities carried
out as a corporate are limited to land production factors, seeding, planting, and harvesting,
while maintenance is left to each farmer. Therefore, the use of input quantities of fertilizer,
selection of seeds, and pesticides is in accordance with what the farmers expect. All activities
using mechanization become corporate activities, the rest that does not use mechanization
become individual non-corporate activities. Thus, the focus of implementing the corporate
farming model is on the efficient use of agricultural tools and machinery in reducing the use
of the number of workers so that cost efficiency is achieved in addition to the increasingly
limited availability of labor.
The corporate farming model of rice farming activities begins with the merging of land
simultaneously to eliminate land boundaries in order to facilitate mechanization. The land
boundaries were previously marked with stakes as the boundaries of land ownership. Land
processing uses a four-wheeled tractor. Seedlings use grain seeders with a tray or dapog.
Cultivation is carried out twice as often as the growing season in one year. This means that
farmers plant rice continuously for two seasons without rotation. Planting was carried out
simultaneously using a rice transplanter. The spacing is set directly at the time of planting 30
cm x 15 cm. Harvest uses a combine harvester.
Allocation of Use of Production Factors
The production factors used in the study were land area, urea fertilizer, SP-36 fertilizer,
NPK fertilizer, pesticides, and labor. Production factors carried out in a corporate manner
include land and labor. The average allocation for the use of production factors can be seen
in Table 1. The average land area for corporate farming farmers in Sukoharjo Regency is 0.42
ha and is classified as still narrow. In line with the opinion of Susilowati & Maulana (2016),
the average agricultural land ownership ranges from 0.1 to 0.4 ha per farmer household.
The majority of farmers in the corporate farming use three types of macro fertilizers,
namely Urea fertilizer, SP-36 fertilizer, and NPK fertilizer. The average use of Urea fertilizer is
263.01 (kg/ha), SP-36 fertilizer 233.73 (kg/ha) and NPK fertilizer 322.30 (kg/ha). The amount
of fertilizer used has exceeded the recommended location-specific dosage. The recommended
dosage for fertilizer in Sukoharjo Regency is Urea 250 (kg/ha), SP-36 75 (kg/ha) while NPK
50 (kg/ha) (Apriyantono, 2007). The habit of using fertilizers in large quantities is generally
influenced by farmers' perceptions; the use of more production factors gives the possibility of
high production. Even though rice plants absorb nutrients as needed, excessive provision
results in the soil which becomes acidic, the ability to absorb nutrients is inhibited and the
cost of farming is higher (Riyadi, Hartono, & Andri, 2015).
The use of pesticides by farmers varies, the average use of pesticides is 2.5 liters/ha.
Pest attacks that often occur in corporate farming include stem borer, stinkbug, brown plant
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hopper, rats, and birds. Spraying is carried out 3-5 times per growing season according to the
attack and variety of pests and diseases. The more intensity of spraying, of course, harms plants
and the environment. Even though the use of active ingredients is low, exceeding the
threshold can harm farmers because they threaten the existence of natural enemies which
leads to the existence of more pest attacks (Yuliana, Ekowati, & Handayani, 2017). The
average use of the corporate farming labor force in Sukoharjo Regency as a whole is 14.81
HOK. The labor production factor that becomes the research variable is labor that is not
together so that the use of the number of workers is relatively smaller. The allocation of the
use of labor in the family of 2.73 HOK or 19% is used in semi-corporate farming activities
while the remaining 81% of the workforce is outside the family activities together.
TABLE 1.ALLOCATION OF THE USE OF RICE PRODUCTION FACTORS FOR CORPORATE FARMING IN SUKOHARJO REGENCY 2018-2019
Production Factors Unit Average
Land area Ha 0.42
Urea fertilizer Kg 263.01
SP-36 fertilizer Kg 233.73
NPK fertilizer Kg 322.30
Pesticide Liter 2.5
Labor HOK 14.81
Source: Primary Data Analysis
Factors that Affected Rice Production
The results of data processing using the 4.1c frontier program provide estimates of
regression parameters using the Maximum Likelihood Estimation (MLE) method presented
in Table 2. The sigma-squared value (σ^2) of 0.0020 is significant at the α level of 1%. This
means that the error term is normally distributed. The gamma (γ) value of 0.9999 t-ratio value
of 20.763 shows that 99.99% of the residual variation in the model is more dominant due to
technical inefficiency (μi) the remaining 0.01% is due to noise (vi) such as weather, climate,
pests and diseases, natural disasters, etc. If all error terms are caused by noise (vi), the
parameter value of the inefficiency coefficient becomes meaningless (Kusnadi, Tinaprilla,
Susilowati, & Purwoto, 2011). The value of the log-likelihood function MLE (39,123) is
greater than the log-likelihood function OLS (34,090). Therefore, the model used is good
enough to describe the actual conditions of corporate farming.
Measurement of production factors and efficiency uses semi-corporate variables to
obtain measurement variations in the model. It is suspected that the production variables
including land area, urea fertilizer, SP-36 fertilizer, NPK fertilizer, pesticides, and labor have a
significant effect on rice production. Table 2 shows that the variables of land area, urea, and
NPK fertilizer have a significant effect on rice production. The elasticity of production of the
frontier area of land, urea, and NPK fertilizers were 1.1370, -0.1765, and -0.1346, respectively.
The variable of the land area is the most elastic among other variables, meaning that the
increase in farm area can increase the production which is greater than the variables of urea
and NPK fertilizers.
The average land area of 0.42 ha has a significant effect on rice production. The
coefficient of land area production factor is positive according to the expected sign of 0.9486,
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which means that every 1% increase in a land area increases rice production by 0.9486%. In
line with the findings of Kusnadi, Tinaprilla, Susilowati, & Purwoto (2011); Akbar,
Budiraharjo & Mukson (2017); and Suharyanto, Mulyo, Darwanto, & Widodo (2015), the
variable land area is the variable that has the most significant effect on increasing rice
production. The positive influence of land on production was also revealed by Defidelwina,
Jamhari, Waluyati, & Widodo (2019), where the most elastic land area increases rice
production. The wider the cultivated land, the greater the average rice production. The area
of land for farmers to achieve welfare is at least 0.65 ha (Susilowati & Maulana, 2016).
However, the facts in the field show that the average land ownership for corporate farming
farmers is 0.42 ha, meaning that there is still a difference of 0.23 ha from the minimum limit.
One of the reasons for the size and structure of farmers' land tenure that is not yet conducive
is the implementation that has not been fully implemented in a corporate manner.
TABLE 2. RESULTS OF THE STOCHASTIC FRONTIER PRODUCTION FUNCTION OF RICE FARMING CORPORATE FARMING MAXIMUM
LIKELIHOOD ESTIMATION (MLE) METHOD 2018-2019
Source: Primary Data Analysis
***: significant at the 1% level, t-table 1% = 2.6799
**: significant at the 5% level, t-table 5% = 2.0095
Urea fertilizer has a significant effect on rice production, but it is against the expected
signs. The coefficient value of urea fertilizer production factor is -0.1878. This figure means
that with the addition of 1% urea fertilizer, rice production decreased 0.1878%. Urea fertilizer
which reduces production is not in line with the research of Yoko, Syaukat, & Fariyanti
(2014); the addition of Urea fertilizer can still increase low land rice production even though
the increase is low. Rice plants need nutrients in dynamic equilibrium conditions, meaning
that if these nutrients are still deficient or in excess, they affect the increase in yield levels.
This follows the law of diminishing marginal return, increasing the amount of input to a
certain limit can reduce low land rice production. On the other hand, the use of insufficient
inputs is a constraint on production (Riyadi, Hartono, & Andri, 2015).
The NPK fertilizer variable has a significant effect on rice production at an α level of
10%. The regression coefficient of the NPK fertilizer variable was -0.1254, meaning that with
the addition of 1% NPK fertilizer, rice production decreased by 0.1254%. This result is
Variable Expectation Value Coefficient Std.Error t-ratio
Constant +/- 0.2992 0.6678 0.4480
Ln land area + 1.1370*** 0.1204 9.4404
Ln Urea fertilizer + -0.1765** 0.0705 -2.5010
Ln SP-36 fertilizer + -0.0379 0.0387 -0.9779
Ln NPK fertilizer + -0.1346** 0.0610 -2.2069
Ln pesticide + 0.0135 0.0316 0.4271
Ln labor + 0.0140 0.0440 0.3193
Sigma-squared 0.0020 0.0031 6.3909
Gamma 0.9999 0.0000 41.853
Log likelihood function OLS 34.090
Log likelihood function MLE 39.123
LR test of the one-sided error 10.066
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contrary to the findings of Yuliana, Ekowati, & Handayani (2017), which states that the use
of NPK fertilizer (phonska) has a positive effect on production, which means that the addition
of NPK fertilizer can still increase rice production. The average use of NPK fertilizer for
corporate farming farmers is 322.30 kg/ha. This amount exceeds the recommended dosage
for areas where NPK fertilizer according to the principle of balanced fertilization is 50 kg/ha
(Apriyantono, 2007). The farmers' habit of using macro fertilizers massively per planting
season has resulted in high soil residues while the previous fertilization nutrients have not
broken down completely. The use of chemical fertilizers without being followed by
rehabilitation might create the possible risk such as the degradation of agricultural land, and
ultimately this causes a decrease in production (Riyadi, Hartono, & Andri, 2015).
Technical, Economic, and Allocative Efficiency
Research on the corporate farming model previously described by Lerman & Sedik
(2007) uses three input models (land and labor), input (land, labor, and number of agricultural
machinery) and inputs (land, labor, number of agricultural machines and nurseries),
agriculture. The corporate model is more efficient than non-corporate agriculture with the
efficiency value of the corporate model agriculture (> 0.70) while the non-corporate agriculture
(<0.70). This result is not in line with the findings of Kostov, Davidova, & Bailey (2019), using
non-corporate agricultural labor input more efficiently than corporate model agriculture in
several countries such as Romania, Spain, Hungary, and the Czech Republic. Furthermore,
Kostov, Davidova, & Bailey (2019) stated that traditional agriculture is the foundation of the
family economy (subsistence) so that they are more serious in achieving their efficiency. This
efficiency can be increased by increasing farm managerial abilities (Kostov, Davidova, &
Bailey, 2019). Meanwhile, the corporate farming model in Sukoharjo Regency based on
stochastic frontier analysis is not technically, economically, and allocatively efficient. This
result is in line with the findings of Kostov, Davidova, & Bailey (2019) where the corporate
model of agriculture is not yet fully efficient.
TABLE 3. DISTRIBUTION OF TECHNICAL, ECONOMIC AND ALLOCATIVE EFFICIENCY VALUES OF CORPORATE FARMING IN
SUKOHARJO REGENCY 2018-2019
Source: Primary Data Analysis
On average, farmers of the corporate farming achieve a technical efficiency of 0.75
from their frontier with the best management system (the best practice). The higher the
Efficiency Technical Economic Allocative
Number % Number % Number %
0,50 ≤ E < 0,60 4 0.00 2 3.92 0 0
0,60 ≤ E < 0,70 4 1.96 3 5.88 0 0
0,70 ≤ E < 0,80 9 17.65 11 21.57 0 0
0,80 ≤ E < 0,90 13 25.49 16 31.37 4 7.84
0,90 ≤ E < 1,00 28 54.90 19 37.25 6 11.76
> 1,00 0 0 0 0 41 80.40
Total 51 100.00 51 100.00 51 100.00
Minimum 0.56 0.55 0.80
Maximum 0.99 0.99 1.65
Average 0.75 0.84 1.12
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efficiency value, the more increasingly difficult the opportunity to increase production (Yoko,
Syaukat, & Fariyanti, 2014). Corporate rice farming still has the opportunity to increase
production by 15% from the frontier by optimizing the use of production factors, while others
still require technological innovation and farm managerial improvements (Kostov, Davidova,
& Bailey, 2019).
Economic efficiency is estimated from the input and output side of the production at
the average price at the farmer level. The price of each input and output includes the average
price of dry unhulled rice which was 4,017.65 (IDR/kg). The average price of urea fertilizer
was 1,901 (IDR/kg). The average price of SP-36 fertilizer was 2,309 (IDR/kg). The average
price of NPK fertilizer was 2,464 (IDR/kg). The average price of pesticides was 274,034
(IDR/lt) and the average labor price was 59,226 (IDR/HOK). The average economic efficiency
of corporate rice farming is 0.84. If the average farmer can achieve the highest degree of
economic efficiency, it can provide costs of 1- (0.84/0.99) or 15%. Ineffective farmers can
charge 1- (0.55/0.99) or 45%. Meanwhile, the average allocative efficiency of rice farming in
the agricultural companies of the Sukoharjo Regency is 1.12. If the average farmer can achieve
allocative efficiency, it can guarantee costs 32% 1- (1.12/1.65), while the least efficient farmers
can provide costs 52% 1- (0.80/1.65).
It is indicated by field conditions that farmers of the corporate farming are not yet
cost-oriented. This is indicated by the excessive use of production factors. Cost efficiency has
not been achieved. When viewed from the structure of the cost of rice farming, the
expenditure for mechanization reaches 57% and the remaining 43% is the expenditure on
fertilizer production factors (Urea, SP-36, and NPK), seeds, and pesticides. Land processing
by using a four-wheeled tractor is carried out after the land boundaries are leveled. The cost
of cultivating by using a four-wheeled tractor is 875,585 (IDR/ha). The average cost ranges for
seeding using a grain seeder of 1,000,000 (IDR/ha). Sowing seeds using a grain seeder aims
to obtain uniform seeds to facilitate planting using a rice transplanter. Planting seeds by using
a rice transplanter is done after the seedlings are not less than 14 days old, and planting them
simultaneously in order to anticipate the attack of plant pests that cannot be predicted. The
average land area of 0.42 ha requires at least 100 seed trays. The cost of planting with
individual farmers' rice transplanters was 1,497,326 (IDR/ha), and harvesting using a
combine harvester cost 1,996,435 (IDR/ha).
One-sample t-test was used to determine whether the corporate rice farming has or has
not been efficient technically, economically, and allocatively. If the p-value is greater than α
5%, or if the t-count is smaller than the t-table, the rice farming is efficient. Conversely, if the
p-value is smaller than α 5%, or the t-count is greater than the t-table, the rice farming is not
efficient. The results of the analysis of the average difference test on technical, economic, and
allocative efficiency show that the p-value (0.000) is smaller than the α value of 5% (Table 4).
This means that the average value of technical efficiency is 0.75, the economic efficiency is
0.84, and the allocative efficiency is 1.12, which are significantly different from the value of
one. Thus, the corporate farming of the Sukoharjo Regency is not yet efficient.
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TABLE 4. DIFFERENT TEST FOR AVERAGE TECHNICAL, ECONOMIC AND ALLOCATIVE EFFICIENCY AGAINST VALUE 1 IN 2018-2019
Source: Primary Data Analysis
***: significant at the 1% level
Technical Inefficiency
The results of technical efficiency analysis show that, in the model, there is still a
technical inefficiency problem in production by 15%. It is suspected that several internal
factors are originating from the socio-economic characteristics that are the source of technical
inefficiency in the corporate farming model of rice farming. Therefore, in order to find out
the source of inefficiency in the corporate farming in Sukoharjo Regency, an analysis of
sources of technical inefficiency was conducted. The analysis of sources of technical
inefficiency used a frontier stochastic production function model. The results of the analysis
of the frontier stochastic production function on the factors that affect the inefficiency of
corporate farming show that the variables of education, number of family members, and
extension have significantly increased efficiency (Table 5).
The age variable has a negative but not significant effect on inefficiency with an
estimated coefficient of -0.0122 which means that the age of the farmer does not have a
statistical effect on efficiency. Respondent farmers are heads of farm families who are
managers and cultivators of corporate land whose age (73%) is still relatively young. This
proves that the corporate farming model is in great demand by young farmers and tends to
produce more efficient farming (Kusnadi, Tinaprilla, Susilowati, & Purwoto (2011). This
result is contrary to the findings of Yoko, Syaukat, & Fariyanti (2014) and Suharyanto, Mulyo.
Darwanto, & Widodo (2015) stated that age has a significant effect on inefficiency, the older
the farmer, the more increasing the level of farming inefficiency.
Education has a significant negative effect on inefficiency with an estimated coefficient
of -0.1957. This means that farmers with higher level of education are more efficient than
farmers who have never received formal education. The condition in the field shows that 60%
of farmers' education is low so that it becomes an inefficiency problem, and this is the basis
for government policy to improve education and managerial skills of farmers (Kusnadi,
Tinaprilla, Susilowati, & Purwoto, 2011). Farming management through corporate farming
becomes non-formal education to increase knowledge and technical skills in farming. It is
hoped that farmers with low education will become members of corporate farming, and
information is more available and easier to obtain. They are also expected to be open to new
technology. Besides, corporate farming can be used as a medium for exchanging experiences
so that illiterate farmers can learn from fellow farmers. The negative effect of education on
inefficiency is in line with the findings by Kusnadi, Tinaprilla, Susilowati, & Purwoto (2011)
and Suharyanto, Mulyo, Darwanto, & Widodo (2015), that education has a significant
Efficiency t Df Sig.(2-tailed) Mean Difference Lower Upper
Technical -15.894 50 0.000 -0.2422 -0.2728 -0.2116
Economic -10.246 50 0.000 -0.1613 -0.1924 -0.1302
Allocative 5.145 50 0.000 0.1178 0.0718 0.1638
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164 AGRARIS: Journal of Agribusiness and Rural Development Research
negative effect at the α level of 5% which means the higher the education of rice farming
farmers, the more efficient they are.
TABLE 5. RESULTS OF THE STOCHASTIC FRONTIER PRODUCTION FUNCTION AGAINST FACTORS AFFECTING THE TECHNICAL
INEFFICIENCY OF RICE FARMING CORPORATE FARMING IN SUKOHARJO REGENCY 2018-2019
Source: Primary Data Analysis
***: significant at the 1% level, t-table 1% = 2.6799
**: significant at the 5 % level, t-table 5% = 2.0095
*: significant at the 10% level, t-table 10% = 1.6765
Experience has no significant effect on inefficiency with a coefficient of -0.0136. This
means that the length of the farming experience does not count towards inefficiency. This
shows that rice farming is a hereditary agricultural model that has become a habit without
providing agents of change in terms of farming efficiency. The results are contrary to the
research by Suharyanto, Mulyo, Darwanto, & Widodo (2015), that experience has a real effect
on efficiency where increased farming experience reduces inefficiency because experience
becomes a source of good cultivation knowledge through the fellow farmers’ experience.
The number of family members has a significant negative effect on inefficiency with a
coefficient value of -0.1054. This means that if there are more family members in the
productive age, the rice farming is technically more efficient. It is in line with the findings of
Yoko, Syaukat, & Fariyanti (2014), where the number of families in the productive age tends
to be more efficient than farmers with a few productive family members. Based on the field
results, the average number of farmer’s family members is 2 to 3 people. Productive family
members of corporate rice farming are used for activities that are not carried out collectively,
such as weeding, fertilizing, and maintaining. The rest are used outside family labor.
The extension has a significant effect on inefficiency with a negative coefficient of -
0.1251. This figure means that extension is a factor to increase farm efficiency. The higher the
frequency of farmers attending counseling, the more information, and knowledge they have
in their farm managerial. This is in line with the findings of Prayoga, (2010); Tsoho,
Omotesho, Salau, & Adewumi (2012), and Yoko, Syaukat, & Fariyanti (2014), that extension
variables are negatively related to inefficiency. Based on field conditions, 88% of farmers
actively participate in extension services, while the remaining 12% have never attended
extension services. Farmers who do not participate in the counseling are elderly farmers and
those who make the agricultural sector as a side job. The average frequency of farmers
participating in extension is 2 times. Most of the counseling was carried out by the Agriculture
Office of Sukoharjo Regency, field extension officers (PPL), agricultural extension offices,
fisheries and maritime offices, Bank of Indonesia, and the Ministry of Agriculture. Field
Variable Expectation Value Coefficient Std.Error t-ratio
Constant +/- 1.1332 0.7642 1.4828
Ln age + -0.0122 0.1869 -0.0656
Ln education - -0.1957*** 0.0570 -3.4304
Ln experience - -0.0136 0.0609 -0.2239
Ln members of family farmer’s - -0.1054** 0.0508 -2.0714
Ln extension - -0.1251* 0.0709 -1.7637
(Dummy) membership actively - -0.0338 0.0708 -0.4780
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165 Efficiency of Rice Farming ….. (Iskandar et al)
extension officers are the ones who provide the most counseling to farmers of the corporate
farming rice, either directly on stretches of land or through farmers group union and farmer
groups.
The group activeness dummy has no significant effect with the estimated coefficient
of -0.0338 which indicates that whether or not the group members are active does affect
inefficiency. The field conditions indicate that corporate farming members are most active in
the activities of the invited farmer groups. Representatives of members who actively participate
in group activities provide information to fellow corporate farmers. However, the corporate
farming model has not been fully accepted. They need corporate membership only for the
sake of receiving assistance so they feel they need to become members. This is not in line with
the findings of Kusnadi, Tinaprilla, Susilowati, & Purwoto (2011), that group membership
variables have a significant effect on inefficiency, where the participation of farmers in
activities will increase inefficiency.
CONCLUSIONS
The corporate farming model of rice production is significantly influenced by the
variable of production factors for land area, urea, and NPK fertilizer, while SP-36 fertilizer,
pesticides, and labor do not affect rice production. Of all the variables of production factors
for land area, pesticides, and labor, it shows a positive coefficient of estimation, meaning that
the use of production factors for land area, pesticides, and labor can increase rice production.
Urea, SP-36, and NPK fertilizers show a negative regression coefficient on production, which
means that the use of fertilizers can reduce rice production so that the use of fertilizers needs
to be reduced.
The analysis of the efficiency of corporate farming in the Sukoharjo Regency is not
technically, economically, and allocatively efficient. The average index of technical, economic,
and allocative efficiency were 0.75, 0.84, and 1.12, which differed significantly from the value
of one. This means that in order to achieve the highest degree of technical, economic, and
allocative efficiency, it is necessary for managerial to reduce or to add production factors.
Thus, intensification through corporate farming is still possible through optimizing the use of
basic technology. Besides, the need to reduce the operational costs of land processing tools
and machines, seeding, planting, and harvesting is still high so that cost-efficiency can be
achieved.
All variables that are thought to affect inefficiency show a negative regression
coefficient which means that all variables together can reduce inefficiency. Socio-economic
factors that significantly reduce inefficiency are education, number of family members, and
counseling, while social factors; age, experience, and activity dummy in groups do not have a
significant effect on the technical inefficiency of corporate farming.
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