ISSN: 2407-814X (p); 2527-9238 (e)
AGRARIS: Journal of Agribusiness
and Rural Development Research
Vol. 7 No. 2 July – December 2021,
Pages: 142-159
Article history:
Submitted : February 26th, 2021
Revised : August 2nd, 2021
June 8th, 2021
Accepted : August 11th, 2021
Rokhani1,*, Ahmad Asrofi2, Ad Hariyanto Adi3, Ahmad Fatikhul
Khasan3, Mohammad Rondhi2
1 Department of Agricultural Extension, University of Jember, East
Java, Indonesia
2 Department of Agribusiness, University of Jember, East Java, Indonesia
3 Performa Cendekia, East Java, Indonesia
*) Correspondence email: rokhani@unej.ac.id
The Effect of Agricultural Extension Access on The Performance
of Smallholder Sugarcane Farmers in Indonesia
DOI: https://doi.org/10.18196/agraris.v7i2.11224
ABSTRACT
Agricultural extension plays a crucial role in the Indonesian Agricultural
Revitalization Program for the 2005-2025 periods, where sugarcane is one of the
fourteen priority crops. The provision of an agricultural extension was aimed to
increase the income and productivity of sugarcane farmers. This study aimed to
evaluate the effect of agricultural extension access on smallholder sugarcane
farmers' performance in Indonesia. This study used data from the 2014
Indonesian Sugarcane Farm Household Survey, consisting of 8,831 farmers.
This study employed propensity score matching to estimate the effect of access
to an agricultural extension on several outcome variables. These variables were
gross value-added (GVA), net value added (NVA), labor productivity (LP), land
productivity (LDP), net income (NI), and remuneration of family labor (ROFL).
The result shows that having access to an agricultural extension increases GVA
by 40.5%, NVA by 40.3%, labor productivity by 42.8%, and NI by 40.2%.
However, access to agricultural extension insignificantly affects ROFL due to the
differences in family working units. Also, farmers with Agricultural Extension
access have 13.7% lower land productivity than non-Agricultural Extension
farmers since the former has lower input use intensity than the latter. These
results suggest that providing agricultural extension service is adequate to
improve sugarcane farmers' economic performance.
Keywords: Agricultural extension, farm gross value-added, farm net value-added,
net income, remuneration of family labour
INTRODUCTION
Agricultural Extension (AE) plays a crucial role in improving farmers' managerial and
technical capacity (MTC) (Bhatta, Ishida, Taniguchi, & Sharma, 2008; Hansson, 2008). An
improved farmer's MTC is essential to increase farm production, minimize yield loss due to
better pest management, and foster technology adoption that further increases farm
productivity (Hansson, 2008). Thus, AE has a strategic role at the macro level to improve
agricultural productivity and improve farm performance and farmer welfare at the micro-
level. In Indonesia, AE is an integral part of the Agricultural Revitalization Program (ARP)
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mailto:rokhani@unej.ac.id
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143 The Effect of Agricultural Extension Access….. (Rokhani, Asrofi, Adi, Khasan, and Rondhi)
for the 2005-2025 periods. The government legalized Act 16/2006 to establish the national
Agricultural Extension System (AES). The AES establishment aims to achieve the ARP-2025
goals of improving farm performance and farmer welfare for 14 strategic commodities.
Sugarcane is a strategic commodity that serves as the primary raw material for the
Indonesian sugar industry and livelihood source for 287,099 farm households (BPS-Statistics
Indonesia, 2019). However, the lack of high-quality seed plants and the inefficient farm is
currently causing the low productivity of sugarcane farmers (Toharisman, Triantarti, &
Hasan, 2013). AE plays a crucial role in solving this challenge since it increases farmers'
probability of adopting high-quality seeds and improving farming practices (Suwandari et al.,
2020). Thus, evaluating how AE improves farm performance and farmer welfare is crucial.
Various studies have identified how AE affects farm performance in different
countries, utilizing nationally representative farm data. Ragasa and Mazunda (2018), using a
national household panel survey from the Government of Malawi, found that AE is strongly
associated with maize and legume farmers' productivity. The BRAC extension program
improves farmers' basic cultivation methods in Uganda, increasing productivity gains from
the same farm inputs quality (Pan, Smith, & Sulaiman, 2018). Similarly, Cunguara and
Moder (2011), utilizing the National Agricultural Survey of 2005 in Mozambique, found
that AE increases farm productivity by 11%, but the extension officers tend to choose
wealthy farmers, potentially increasing income inequality in the rural area. Also, Emmanuel,
Owusu-Sekyere,, Owusu, & Jordaan (2016) used the 2011 Ghana Agricultural Production
Survey to estimate the effect of AE on rice farmers' productivity. They found that AE
increases rice farm productivity through increased chemical fertilizer application. Finally, a
study using the Teagasc National Farm Survey found that AE increases Irish farmers'
income. These findings demonstrate that AE significantly improves farm productivity and
income (Cawley, O'Donoghue, Heanue, Hilliard, & Sheehan, 2018). However, it potentially
increases rural income inequality and environmental damages through increasing chemical
inputs use.
To date, few studies evaluate the Indonesian AES policy using appropriate and
nationally representative farm data. The majority of AE-related studies in Indonesia is case
studies in nature and did not use comprehensive farm performance variables. Examples of
those studies are Wardana and Sunaryanto (2019), who studied rice farmers perception
toward AE and its impact on their welfare in Semarang, Yunita, Satmoko, & Roessali
(2018), who studied the role of AE on the adoption of Integrated Crop Management by rice
farmers in Magelang, and Prihatin, Aprolita, & Suratno (2018) who studied the effect of AE
on-farm labor productivity in vegetable farming in Muaro Jambi. These studies found that
AE positively impacts farm performance. An exception to the previous studies is
(Indraningsih, 2015), who conducted an ethnomethodology study on Indonesian AES's
governing bodies in Java, Sumatra, and Sulawesi. The study found that extension policies
increase rice productivity by 29-32.7%.
Based on that background, this study aimed to evaluate the effect of AE on the
performance of smallholder sugarcane farmers in Indonesia. The study used data from the
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2014 Indonesian Plantation Farm Household Survey, consisting of 8,831 farmers. The study
used comprehensive performance variables representing farm value-added (Gross Value-
Added and Net Value-Added), farm productivity (labor and land productivity), farm income,
and family labor remuneration. These variables effectively measure farm economic
performance but are rarely used in AE-evaluation studies, particularly in Indonesia. A
comprehensive economic evaluation of AE is needed to understand how AE affects and
improves farm performance. The primary contribution of this study is to inform whether
and how much AE affects the performance of smallholder sugarcane farmers in Indonesia.
RESEARCH METHOD
Research Design
This study used a Mixed-Method Sequential Explanatory Approach (M-MSEA) design
to estimate the effect of AE access on farmer performance. M-MSEA consists of two
analytical stages, the quantitative and qualitative stages (Creswell, 2013). The M-MSEA aims
to obtain generalizable findings from the quantitative stage backed with the qualitative
stage's explanations. In the quantitative stage, we estimated AE access on smallholder
sugarcane farmers' performance using nationally representative data of 8,831 farmers. Figure
1 shows the distribution of farmers. In the qualitative stage, we explored the mechanism by
which AE was delivered to farmers. In the second stage, we conducted in-depth interviews
with sugarcane farmers, government extension officers, and private enterprise extension
officers. The interviews were conducted at Malang regency, East Java, in November 2020.
FIGURE 1. THE DISTRIBUTION OF SMALLHOLDER SUGARCANE FARMERS IN INDONESIA
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145 The Effect of Agricultural Extension Access….. (Rokhani, Asrofi, Adi, Khasan, and Rondhi)
Data
This study used nationally representative data of sugarcane farmers in Indonesia for
the quantitative stage. The data was the result of the 2014 Indonesian Plantation Farm
Household Survey (IPFHS). The IPFHS data is appropriate for this study for three reasons.
First, the survey was conducted in 2014 and can capture the first half of ARP2025. Second,
the data has nationwide coverage of sugarcane farmers; thus, it accurately measures the
national agricultural extension program's outcome. Third, the data cover comprehensive
socio-economic information of sugarcane farmers, making it possible to perform a thorough
farm economic assessment. The data consists of 8,831 sugarcane farmers distributed in eight
provinces (Figure 1). The figure shows that most sugarcane farmers are located in East Java
(59.80%) and Central Java (35.62%). The total number of farmers in both provinces
accounts for 95.42% of total sugarcane farmers in Indonesia. The rest of the farmers are
located in Sulawesi Island (2.15%), Sumatera Island (1.03%), West Java (0.85%), and
Yogyakarta (0.54%).
Estimating the effect of AE access on smallholder sugarcane farmers performance
The estimation of the AE effect on farmer performance consisted of two phases. First,
we assessed farm economic performance using the Farm Accountancy Data Network
(FADN) framework. The framework provides comprehensive indicators to measure the
value-added and profit from farming activity, labour productivity, and family labour
remuneration. For that purpose, this study used five indicators: farm gross value-added, farm
net value-added, labour productivity, net farm income, and remuneration of family labour.
The calculation of these indicators used variables in Table 1 (coded 2). Then, these
indicators will be used as outcome variables in the second phase of quantitative analysis.
Table 2 shows the formula for each indicator.
TABLE 1. THE FARM PERFORMANCE INDICATORS
Variable Unit Description Formula
FGVA IDR/yr Farmer Gross Value Added Output + Government Support – Intermediate Consumption
FNVA IDR/yr Farmer Net Value Added FGVA – Tax – Depreciation
LP IDR/AWU Labour Productivity FNVA / AWU
LDP IDR/Ha Land Productivity FNVA/UAA
FNI IDR/yr Farm Net Income
FNVA – Total External Factors + Balances of Government Support and Taxes
Investment
RoFL IDR/AFWU Remuneration of Family Labour FNI – Opportunity Cost of Own Land – Opportunity Cost of Own Capital
Note:
1. FGVA: Farm Gross Value-Added, FNVA: Farm Net Value-Added, LP: Labour Productivity, LDP: Land Productivity, FNI: Farm Net Income,
RoFL: Remuneration of Family Labour.
2. AWU is an annual working unit, and AFWU is an annual family working unit
We estimated the average treatment effect on the treated (ATT) of AE access on
smallholder sugarcane farmer's performance in the second quantitative analysis phase. The
ATT has been widely used to estimate the effect of an intervention using observational data.
The ATT is the expected value difference between the outcome variables of the control and
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treated group. Since we used observational data, we assumed that the data meet conditional
independence and overlapping assumptions. Then, we estimated the ATT using propensity
score matching analysis (PSM). Equation 1 denotes the estimation formula in PSM.
𝐴𝑇𝑇 = 𝐸(𝑦𝑖𝑗 |𝐷𝑗 = 1, 𝑝(𝑥𝑖𝑗 )) − 𝐸(𝑦𝑜𝑗 |𝐷𝑗 = 0, 𝑝 (𝑥𝑖𝑗 )) (1)
Where E(●) denotes the expected value of the outcome variables for farmers with AE access (y1j) and
farmers with no AE access (y0j).
The PSM estimates the farmers' probability of accessing AE using several observable
characteristics (xij). The probability values were obtained from propensity scores generated
using a logistic regression model (LRM). The dependent variable in the LRM is farmer's
access to AE, and the independent variables consisted of eight socio-economic variables
shown in Table 1 (coded 1). Equation 2 denotes the formula to estimate the LRM.
8
0 i i
b 0
8
0 i i
b 0
b b x
i
i
b b xi
p e
Y ln , i 1, 2, ,8
1 p
1 e
=
=
+
+
= = =
−
+
K (2)
Yi is farmer access to AE (1=have access, 0=have no access), b0 is the regression constant, bi is the
parameter to be estimated, and xi is the independent variable.
The likelihood ratio test and pseudo R2 were used to check the robustness of the
LRM. Before estimating the ATT, a balance test was conducted to test the balance between
the control and treated groups. A balance test was performed to make a relevant comparison
group and decide the appropriate matching algorithm (Baser, 2006). We estimate the ATT
using the radius matching algorithm. This algorithm is appropriate because it matched each
observation in the treated group with those in the control group, which propensity scores
within a predefined radius (Dehejia & Wahba, 2002). That way, more observations from the
control will be used if suitable matches are available, and fewer observations will be used if
suitable matches are not available.
RESULTS AND DISCUSSION
Result
Socio-economic characteristics of Indonesian smallholder sugarcane farmers
This study used data from the 2014 Indonesian Plantation Farm Household Survey
for sugarcane. The data cover farmers in eight provinces and have a wide range of socio-
economic characteristics. The variables cover the social (age, education, gender), economic
(utilized agricultural area, farm capital, wealth), and institutional (government support and
contract farming) aspect of sugarcane farming. Also, the data cover the farming aspect such
as farm production, farm labor, seed plant, fertilizer, and pesticide. Table 2 summarizes the
socio-economic characteristics of smallholder sugarcane farmers in Indonesia.
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147 The Effect of Agricultural Extension Access….. (Rokhani, Asrofi, Adi, Khasan, and Rondhi)
TABLE 2. SOCIO-ECONOMICS CHARACTERISTICS OF INDONESIAN SUGARCANE FARMERS
Code Variable
Have access to AE Have no access to AE
Mean SD. Freq.1 Mean SD. Freq.
1 Age (yr) 50.7 11.1 51.8 11.9
1 Education
Elementary 872 (63.05) 5,416 (72.72)
Middle 436 (31.53) 1,815 (24.37)
High 75 (5.42) 217 (2.91)
1 Gender
Female 78 (5.64) 779 (10.46)
Male 1,305 (94.36) 6,669 (89.54)
1 UAA (ha) 22.48 70.47 0.72 21.04
1 Capital (IDR)4 45,882.9 17,1015.7 10,669.5 39,659
1 Wealth
Poor 360 (26.03) 2,181 (29.28)
Wealthy 1,023 (73.97) 5,267 (70.72)
1 Government Support
Not Receive 765 (55.31) 4,062 (54.54)
Receive 618 (44.69) 3,386 (45.46)
1 Contract Farming
Not Participate 466 (33.70) 5,329 (71.55)
Participate 917 (66.31) 2,119 (28.45)
2 Production (kg) 78,238.3 28,3027.5 24,764.6 88,857.8
2 Hired Labour 27.2 70.3 10 70.1
2 Family Labour 2.8 5.4 1.8 5.5
2 Seed Plant 31,038.3 59,132.1 14,509 59,272.1
2 Fertilizer
Urea 100 333.6 69.6 334.2
TSP/SP36 78.6 395.3 24.7 396.2
Za 1,416 2,684.3 487.9 2,689.3
KCl 32.2 161 10.4 161.7
NPK 719 1,612.2 221 1,614.8
Organic 641.8 1,798.3 272.7 1,801.1
2 Pesticide
Solid 0.1 195.3 2.8 195.7
Liquid 213.5 2,050.9 51.8 2,054.9
2 Growth Simulator
Solid 0.9 12 0.6 12.1
Liquid 31.5 1,144.3 60 1,146.6
Sample size (n) 1,383 7,448
Note:
1. The value represents the number of the farmer for each category in each group for the categorical variable.
2. Household size is the number of household members (including farmers) in a particular farm household.
The data in Table 2 suggests that young and educated farmers have better access to
AE. On average, a farmer with access to AE is one year younger than those with no AE
access. Also, the educational attainment of farmers with access to AE is higher than their
counterparts. In the former group, 36.95% of farmers attended middle and higher
education, higher than that of the latter, 27.29%. Meanwhile, 63.05% of farmers having
access to AE have elementary education, lower than those who have no access to AE,
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72.72%. But, access to AE seems to be gender-biased since the percentage of female farmers
with access to AE is lower than those with no access to AE. 94.36% of farmers with access to
AE are male, and only 5.64% are female. Meanwhile, in the no access group, 10.46% of
farmers are female.
Furthermore, a cross-tabulation of education, gender, and access to AE (Table 3)
provides a clear insight. The educational attainment of female farmers in both groups is
lower than that of male farmers. In the access group, female farmers with elementary
education are 73.1%, higher than male farmers, 62.5%. Similarly, in the non-access group,
the percentage of female farmers with elementary schooling is 85.2%, higher than that of his
male counterparts, 71.3%. Then, the percentage of female farmers in the middle and higher
education is lower than male farmers in both groups. This data indicates that their low
educational attainment might cause females’ lower access to AE.
TABLE 3. EDUCATION, GENDER AND ACCESS TO AE OF INDONESIAN SUGARCANE FARMERS
Education
Have access to AE Have no access to AE
Male Female Male Female
Elementary 815 (62.5) 57 (73.1) 4,752 (71.3) 664 (85.2)
Middle 417 (31.9) 19 (24.4) 1,709 (25.6) 106 (13.6)
Higher 73 (5.6) 2 (2.5) 208 (3.1) 9 (1.2)
N 1,305 78 6,669 779
Note: The values in the bracket indicate the percentage in each group.
Furthermore, the data strongly suggests that access to AE favors large-scale farmers. On
average, farmers' harvest area with access to AE is 18.7 hectares, higher than those with no
access to AE, 4.5 hectares. Similarly, farmers' farming capital with access to AE is higher
than those with no AE access. The former has annual farming capital of IDR 45,882,900
while the latter has only IDR 10,669,500. Furthermore, 73.97% of farmers with AE access
are wealthy households, slightly higher than those with no AE access, 70.72%. On average,
farmers with AE access have a larger farm size; thus, they have higher input use. The average
hired labor in the farm with AE access is 27.2 per year, higher than that in non-AE access
farms, 10. Also, the former group has a larger household size than the latter. Farmers with
AE access used more seed plants and fertilizer than those with no AE access. Indonesian
smallholder sugarcane farmers commonly use six fertilizer types, and farmers with AE access
use each fertilizer in higher quantity than their counterparts. However, farmers with no AE
access use higher solid pesticide and liquid growth simulators than farmers with AE access.
Farmers with no AE access use 2800% and 190% higher used of solid pesticide and liquid
growth simulators than farmers with AE access, respectively. The percentage of farmers who
receive government support does not differ significantly between the two groups. The data
show that farmers with AE access tend to participate in contract farming (CF). 66.31% of
farmers with AE access participate in CF, higher than that of non-AE access group, 28.45%.
The data imply that the receiving of agricultural extension is related to farmer participation
in CF.
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149 The Effect of Agricultural Extension Access….. (Rokhani, Asrofi, Adi, Khasan, and Rondhi)
The performance of Indonesian smallholder sugarcane farmers
This study aimed to estimate the impact of agricultural extension access on
smallholder sugarcane farmers' performance in Indonesia. To achieve this goal, this study
was divided into two analytical phases. In the first phase, we measured the farmers'
performance using several performance indicators. These indicators were also used as the
outcome variables in the second stage of analysis. Each performance indicator formula is
shown in Table 2, and the components of those indicators are shown in Table 4.
The results demonstrate that farmers with AE access operate at a larger scale than
farmers with no AE access. The AE farmers have higher values than non-AE farmers in three
variables representing the farm size: total output, intermediate consumption, and total
external factors. The first variable is the total output representing the agricultural products'
value being sold and products for farmers' use and consumption. AE farmers' average total
output is IDR 78,793,000/yr, higher than non-AE farmers, IDR 22,231,000/yr. The second
variable is the intermediate consumption that represents the direct and overhead costs of
farm production. On average, AE farmers spend IDR 24,504,000/yr on intermediate
consumption, four times higher than that of non-AE farmers, IDR 6,230,000/yr. Also, AE
farmers use higher total external factors, averaging IDR 609,000/yr than non-AE farmers,
IDR 56,000/yr.
TABLE 4. THE PERFORMANCE OF SMALLHOLDERS SUGARCANE FARMERS IN INDONESIA
Variables
Pooled Access to AE No Access to AE
Mean SD Mean SD Mean SD
Total output (IDR/year) 31,089 126,252 78,793 283,837 22,231 58,718
Intermediate consumption (IDR/year) 9,092 37,291 24,504 85,087 6,230 16,184
Total external factors (IDR/year) 142 4,113 609 9,615 56 1,690
Annual working unit (person) 15 73 30 175 12 26
Family working unit (person) 5 36 11 86 4 11
Tax (IDR/year) 117 437 234 859 97 306
Depreciation (IDR/year) 319 3,154 1,042 7,922 190 651
OC of land (IDR/year) (17,096) 83,795 (46,029) 187,274 (11,724) 40,410
OC of capital (IDR/year) (20,363) 88,326 (49,724) 195,545 (14,911) 44,321
Note:
1. The value is in thousands rupiah.
2. AWU is an annual working unit employed by a farm.
3. FWU is a family working unit.
4. The brackets indicate a negative value.
The average AWU and FWU for AE farmers are 30 and 11 persons, while non-AE
farmers are 12 and 4 persons. It suggests that AE farmers employ two times more farm
labour than non-AE farmers. Furthermore, the AE farmers have higher value both for the
fixed and opportunity costs. There are two fixed-cost in our analysis, farm tax, and
depreciation. The average annual farm tax for AE farmers is IDR 234,000, 240% higher
than non-AE farmers, IDR 97,000. However, the farm tax represents a small percentage of
the total farming costs. The AE farmers have 548% higher depreciation cost than that of
non-AE farmers. The annual depreciation cost for each group is IDR 1,042,000 and IDR
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190,000, respectively. It demonstrates that AE farmers used more capital goods than non-
AE farmers. Finally, the opportunity cost for land and capital of AE farmers is higher than
that of non-AE farmers. Still, both groups' value is negative, indicating that sugarcane
farming is the optimal economic decision for farmers compared to other alternatives.
TABLE 5. THE PERFORMANCE OF FARMERS WITH ACCESS AND NO ACCESS TO AGRICULTURAL EXTENSION
Indicators
Pooled Access to AE No Access to AE
Mean SD Mean SD Mean SD
Farm Gross Value Added (IDR/yr) 21,997 94,327 54,372 210,967 16,001 46,412
Farm Net Value Added (IDR/yr) 21,931 94,758 54,289 209,773 15,907 46,427
Labour Productivity (IDR/AWU) 2,147 5,700 3,709 12,048 1,857 3,323
Land productivity (Income/hectare) 23,717 23,915 21,415 17,734 24,145 24,872
Farm Net Income (IDR/yr) 21,788 93,857 53,762 208,725 15,850 46,208
Remuneration of Family Labour (IDR/FWU) 16,890 76,114 55,215 248,843 21,407 69,275
Sample size (n) 8,831 1383 7,448
Note:
1. The value is in thousands rupiah.
2. AWU is annual working unit employed by a farm.
3. FWU is family working unit.
Table 5 shows the value of each performance indicator. The results further
demonstrate that AE farmers operate a larger farm than non-AE farmers. Furthermore, the
former group records above-average performance while the latter has a below-average
performance. AE farmers produce a higher value-added than non-AE farmers. The average
gross value-added of AE farmers is IDR 54,372,000/year, higher than that of non-AE
farmers, IDR 16,001,000/year. A similar order of comparison is also evident for farm net
value-added, where each group has a value of IDR 54,289,000/year and IDR
15,907,000/year, respectively. The productivity measures indicate that AE farmers have
higher labor productivity, but non-AE farmers have higher land productivity. The average
labor productivity, measured as the net value-added per unit of labor for AE and non-AE
farmers, is IDR 3,709,000 and IDR 1,857,000, respectively. The average land productivity
for the non-AE farmers is IDR 24,145,000/hectare, higher than that of AE farmers, IDR
21,415,000/hectare.
The last two indicators represent the net income for a farm and the remuneration of
each family labour. The results indicate that AE farmers have a higher farm income than
non-AE farmers. AE farmers' average farm income is IDR 53,762,000/year, higher than non-
AE farmers, IDR 15,850,000/yr. Similarly, the family labour of AE farmers receives higher
remuneration than those of non-AE farmers. The average family labour remuneration of AE
and non-AE farmers is IDR 55,215,000/yr and IDR 21,407,000/year, respectively. However,
the mean value of each group does not provide a relevant group of comparison. Thus, the
propensity score analysis is required to create a balanced comparison group.
The Impact of agricultural extension access on the farm performance
The propensity score analysis to estimate the effect of AE on farm performance was
divided into three steps: estimating the propensity score, performing a balance test on
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151 The Effect of Agricultural Extension Access….. (Rokhani, Asrofi, Adi, Khasan, and Rondhi)
covariates, and estimating the ATT (average treatment effect on the treated). A logistic
regression model (LRM) was used to estimate the propensity score of access to AE,
consisting of eight variables. The estimation results show that the model is robust. The
model likelihood ratio is 896.01 and significant at 1% level, and the pseudo R2 is 0.117. Of
the eight variables in the model, six variables significantly affect farmer access to AE. Farm
capital, farmers' education, gender, contract farming, and farmer's wealth increase the
probability of accessing AE. In contrast, being a recipient of government support decreases
the probability of a farmer accessing AE. Finally, the size of the utilized agricultural area has
no significant effect on farmer access to AE. Table 6 summarises the estimation results of
the LRM.
TABLE 6. ESTIMATES OF FACTORS AFFECTING FARMERS ACCESS TO AGRICULTURAL EXTENSION
Variable β SE. Sig.
Utilized agricultural area (ha) 0.01 0.02 0.446
Capital (million rupiah) 0.004 0.009 0.000*
Age (yr) -0.001 0.003 0.825
Education (higher education) 0.237 0.059 0.000*
Gender (man) 0.543 0.128 0.000*
Government Support (receive support) -0.177 0.063 0.005*
Contract Farming (participate) 1.510 0.064 0.000*
Wealth (wealthy) 0.155 0.071 0.029**
Constanta -3.324 0.233 0.000*
Number of obs 8.831
LR chi2 896.01
Prob > chi2 0.000*
Pseudo R2 0.117
Note: *Significant at 10%, **Significant at 5%, ***Significant at 1%, nsnot significant
TABLE 7. THE BALANCE TEST OF COVARIATES IN PROPENSITY SCORE MATCHING
Variable
Unmatched Matched
Bias reduction
AE NAE Sig. AE NAE Sig.
UAA 22489 7220.4 0.000*** 19638 18246 0.524 ns 90.6
Capital 45883 10670 0.000*** 39693 36418 0.563 ns 90.7
Age 50.665 51.767 0.001*** 50.723 50.609 0.792 ns 89.6
Education 1.4237 1.302 0.000*** 1.4141 1.4315 0.442 ns 85.7
Gender 0.9436 0.89541 0.000*** .94273 .94366 0.916 ns 98.1
Government Support 0.44685 0.45462 0.594ns 0.44493 0.4402 0.804 ns 39.0
Contract Farming .66305 0.28451 0.000*** .65786 0.65518 0.252 ns 99.3
Wealth .7397 0 .70717 0.014 .73568 0.747 0.315 ns 65.2
Pseudo R2 0.115 0.000*
Mean bias 24.7 1.7
Median bias 19.8 1.8
Note: *Significant at 10%, **Significant at 5%, ***Significant at 1%, nsnot significant
The second step is the balance test. The purpose of the balance test is to create a
relevant group of comparison between the treated (AE farmers) and the control group (non-
AE farmers). We employed a radius matching algorithm to perform the balance test. The
balance test reduces the selection bias by improving the balance between the treated and
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control groups' explanatory variables. The balance test results demonstrate that all
explanatory variables in the control and treated group do not differ significantly after
matching. Six out of eight explanatory variables were statistically different in the unmatched
panel at the 1% level. After matching, all explanatory variables do not differ significantly
and produce an average of 92.33% bias reduction. Also, the mean and median bias after
matching is only 1.7 and 1.8%. This result indicates that the matching algorithm produces a
relevant comparison group and is suitable for the ATT estimation. Table 7 summaries the
balance test results.
Table 8 shows the ATT estimation results. The ATT estimation results show that AE
farmers have higher gross value-added, net value-added, labor productivity, and farm income
than non-AE farmers. But, the non-AE farmers have significantly higher land productivity
than AE farmers. In contrast, the remuneration of family labour for both groups does not
differ significantly. But, the results indicate that AE farmers remunerate more family labour
than non-AE farmers. The ATT estimation compares AE and non-AE farmers with
statistically indifferent explanatory variables. Thus, the estimation procedure compares
farmers with the same size of land and capital, similar age and level of education, and equal
access to government support and contract farming. Also, both groups have a similar
composition of male and female farmers.
TABLE 8 THE IMPACT OF AE ON THE PERFORMANCE OF SMALLHOLDERS SUGARCANE FARMERS
Variable AE NAE ATT S.E. t-stat
Gross Value-Added 54,372 38,696 15,675 5,731 2.73
Net Value-Added 54,289 38,675 15,613 5,700 2.74
Labour Productivity 3,709 2,595 1,113 329 3.38
Land Productivity 21,278 24,680 -3,401 649 -5.24
Net Income 53,762 38,322 15,440 5,671 2.72
Remuneration of Family Labour 55,215 55,704 -488 6,997 -0.07
Hired Labour 18 11 7 2 2.86
Family Labour 11 5 6 2 2.35
1. The values presented are the average value after matching.
2. The matching algorithm used was Radius Matching.
The results demonstrate that AE farmers produce 40.5% or IDR 15,675,000/year
higher gross value-added (GVA) than non-AE farmers. The average GVA of AE and non-AE
farmers is IDR 54,372,000/year and IDR 38,696,000/year, respectively. Similarly, the net
value-added of AE farmers is 40.3% higher than that of non-AE farmers. The average net
value-added of AE farmers is IDR 54,289,000/year, higher than that of non-AE farmers,
IDR 38,675,000/yr.
Discussions
Access to agricultural extension improves farm performance
The primary purpose of this study is to identify the effect of AE on the performance of
smallholder sugarcane farmers in Indonesia. This study used farm value-added to capture the
added value from sugarcane farming. Farm value-added is crucial in measuring economic
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sustainability since it can measure whether labor, land, and resources used in agricultural
production are optimally remunerated (Thomassen, Dolma, van Calker, & de Boer, 2009).
This study demonstrates that AE increases both the gross and net farm value-added. The
value of FGVA and FNVA is quite similar. This value indicates that the tax and depreciation
costs are relatively small compared to other costs. This study's findings indicate that AE is a
crucial policy instrument to improve smallholder farmers' economic sustainability. The
primary purpose of the Indonesian AES, especially in the sugarcane sector, is to increase
farm productivity (micro-level) to increase aggregate production (macro-level) to support the
national sugar industry.
However, the Indonesian AES covers only 15.16% of farmers. The LRM estimation of
farmer access to AE (Table 1) demonstrates a tendency for extension officers to select only
large-scale farmers. Farmer with higher capital is more likely to gain access to AE. Also,
farmer with a higher household wealth is more likely to have access to AE than those with
lower household wealth. According to Arias, Leguía, & Sy (2013), farm size increases farmer
access to AE since large economies of scale implement feasible new technologies. Farmer
education increases the likelihood of farmer access to AE. This finding conforms to previous
studies in Ghana, where education increases the probability of a farmer accessing AE (
Anang, Bäckman, & Sipiläinen, 2020). Participation in contract farming and government
support increases the probability of access to AE. At the field level, the government uses AE
as a channel to deliver the content of agricultural policy (Bhatta, Ishida, Taniguchi, &
Sharma, 2008). In addition, the result demonstrates that female farmer is less likely to
receive AE than male farmers. Meanwhile, utilized agricultural area and farmer age have no
significant effect on farmer access to AE. Our in-depth interview found that sugarcane
farmers receive extension services mainly from private extension officers. This service is a
feature of contract farming between farmers and sugar mill companies. The companies used
AE as a channel to distribute farm credit and inputs to farmers. This arrangement is typical
in the Indonesian sugarcane sector, where AE is the channel to distribute farm credit to
farmers (Rondhi et al., 2020). Also, AE access is closely related to farmer participation in
contract farming (Rokhani et al., 2020) and adopting a certified seed plant that increases
farm productivity (Suwandari et al., 2020).
Access to AE also increases farm labor productivity by 42.8%; however, it decreases
land productivity by 13.7%. AE farmers' labor productivity is IDR 3,709,000/AWU, higher
than non-AE farmers IDR 2,595,000/AWU. But non-AE farmers have higher land
productivity of IDR 24,680,000/ha than AE farmers, IDR 21,278,000/ha. This finding
aligns with previous literature (Cunguara & Moder, 2011; Emmanuel et al., 2016; Pan et al.,
2018; Ragasa & Mazunda, 2018). However, unlike previous studies, which measure farm
productivity in yield per unit of land, we measured farm productivity as the value-added
created by one unit of labor (labor productivity) and utilized agricultural area (land
productivity). The purpose of using these indicators is to identify whether AE increases farm
productivity through labor or land. The results show that AE significantly increases labor
productivity. It signifies the role of AE as an institution that delivers training to farmers and
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improves their managerial and technical capability. Furthermore, previous studies associated
AE access with technology adoption (Lambrecht, Vanlauwe, & Maertens, 2016; Ogutu et al.,
2020; Pan et al., 2018). It is evident that these technologies, especially farm machinery,
increase labor productivity (Paudel, KC, Rahut, Justice, & McDonald, 2019). However, our
study indicates that AE farmers have lower land productivity than non-AE farmers. The
possible explanation for this finding is that AE farmers, on average, utilized a larger land
area; thus, the input use intensity is lower than those of non-AE farmers. We define input
use intensity as the farm inputs used for each hectare of utilized agricultural area. The data
in Table 9 demonstrate that farmers with no access to AE have higher input use intensity for
all types of inputs. Farmers with access to AE have lower labor, seed, fertilizer, pesticide, and
growth simulator use intensity for each hectare of land. Input use intensity is crucial for
farm productivity. Table 9 shows the average input use intensity between farmers with access
to AE and no AE access.
TABLE 9. ACCESS TO AE AND INPUT USE INTENSITY OF SUGARCANE FARMERS
Variable
Have access to AE Have no access to AE
Mean SD. Mean SD.
Hired Labor (labor/ha) 1.21 3.13 13.89 97.36
Family Labor (labor/ha) 0.12 0.24 2.50 7.64
Seed Plant (seed/ha) 1380.71 2630.43 20151.39 82322.36
Fertilizer 0.00 0.00 0.00 0.00
Urea (kg/ha) 4.45 14.84 96.67 464.17
TSP/SP36 (kg/ha) 3.50 17.58 34.31 550.28
Za (kg/ha) 62.99 119.41 677.64 3735.14
KCl (kg/ha) 1.43 7.16 14.44 224.58
NPK (kg/ha) 31.98 71.72 306.94 2242.78
Organic (kg/ha) 28.55 80.00 378.75 2501.53
Pesticide 0.00 0.00 0.00 0.00
Solid
(kg/ha)
0.00 8.69 3.89 271.81
Liquid (l/ha) 9.50 91.23 71.94 2854.03
Growth Simulator 0.00 0.00 0.00 0.00
Solid
(kg/ha)
0.04 0.53 0.83 16.81
Liquid (l/ha) 1.40 50.90 83.33 1592.50
Furthermore, access to AE significantly increases farm income by 40.2%. AE farmers'
net farm income is IDR 53,762,000/year, higher than non-AE farmers, IDR 38,322,000/yr.
However, both groups have statistically insignificant remuneration of family labour (ROFL).
The ROFL of AE and non-AE farmers is IDR 55,215,000/year and IDR 55,704,000/yr,
respectively. The ROFL value is higher than that of farm income because the opportunity
cost of land and capital is negative. Previous studies have confirmed the income-increasing
effect of AE (Baiyegunhi, Majokweni, & Ferrer, 2019; Cawley et al., 2018; Danso-Abbeam,,
Ehiakpor, & Aidoo, 2018), but these studies measure only farm income and not the
remuneration of family labour. Measuring the effect of AE on family labour remuneration is
crucial to identify whether AE increases farmer welfare and contributes to poverty alleviation
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of the farm household. Thus, we compare family labour remuneration and regional
minimum wages to see whether farm family labour is optimally incentivized.
Does the benefit of AE access equally distributed across the region?
The primary advantage of a nationally representative study is its ability to capture
spatial inequality in the effect of agricultural extension access. The primary purpose of AES
is to improve farm productivity and farmer income in all regions. However, the data shows a
spatial inequality in the AES coverage and AE effect on farmer income. First, the data in
Figure 1 suggest an un-proportionate distribution of extension officials in each province. For
example, farmers with access to AE in East Java and Central Java are only 10.91% and
19.23%, respectively, even though 95.42% of sugarcane farmers are situated in these
provinces. On the other hand, the percentage of farmers with AE access in other provinces
is higher than 20%, such as Sumatera (26.37%), Yogyakarta (64.58%), Sulawesi (60.53%),
and West Java (42.67%). This result indicates that the current distribution of extension
officials is disproportionate to the number of farmers in each province. Thus, a reallocation
of extension officials, especially to the province with the highest number of farmers, will
increase AES coverage. Addressing inequality in agricultural extension is crucial since it
contributed to agricultural productivity (Tsikata, 2015).
Second, the result demonstrates a spatial inequality in income. Figure 2 compares the
family labour remuneration of AE and non-AE farmers to each province's minimum wages.
Family labour remuneration is higher than minimum wages in West Java and East Java, both
for AE and non-AE farmers. These provinces accounted for 60.65% of Indonesian
smallholder sugarcane farmers. The average ROFL of AE farmers in West Java is lower than
that of non-AE farmers. In contrast, AE farmers' average ROFL is higher than that of non-
AE farmers in East Java. Furthermore, only 19% of AE farmers in East Java whose ROFL is
lower than the minimum wages, compared to 21% for the non-AE farmers. A contrasting
situation is found in Central Java, whose farmers account for 35.62% of Indonesian
sugarcane farmers. The average ROFL for AE farmers is lower than that of non-AE farmers
and minimum wages. However, the percentage of AE farmers whose ROFL is lower than
minimum wages is 76%, lower than non-AE farmers, 82%. The ROFL of AE and non-AE
farmers is lower than minimum wages in Yogyakarta, Lampung and North Sumatera. But,
the farmers in these three provinces are only 1.57% of total farmers.
This finding provides essential information on the Indonesian Agricultural
Revitalization Program (ARP) evaluation, especially on the Indonesian Agricultural
Extension System (AES). In general, the Indonesian AES effectively increases both the
productivity and income of smallholders of sugarcane farmers. However, there are two
situations worthy of consideration. First, the data imply that there is a tendency for the
extension officers tends to select larger farmers. This tendency makes the AES less inclusive
since the majority of farmers are small-scale farmers. Second, the coverage of extension
services must be increased proportionally to the population in each province. For example,
the AES coverage in East Java, whose farmers account for 59.80% of total farmers, is only
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10.91%. Meanwhile, the AES coverage in Sulawesi, whose farmers are only 2.15% of total
farmers, is 60.53%. In this case, the government should allocate AES's coverage
proportionately to the number of farmers, which can be achieved by equating AES's percent
coverage in each province.
FIGURE 2. FAMILY LABOUR REMUNERATION AND REGIONAL MINIMUM WAGES BETWEEN AE AND NON-AE FARMERS
CONCLUSIONS
This study aimed to estimate the impact of agricultural extension access on
smallholder sugarcane farmers' performance in Indonesia. The results of this study show
that AE access significantly improves farm performance. First, farmers with access to AE
produce 40.5% (gross) and 40.3% (net) higher value-added than those with no AE access.
Second, access to AE increases labor productivity by 42.8%, but it decreases land
productivity by 13.7%. Third, agricultural extension access increases farm income by 40.2%.
The remuneration of family labour doesn't differ significantly between AE and non-AE
farmers, but the former remunerates more family labour than the latter. However, the study
found that the Indonesian AES prioritizes larger farmers than small-scale farmers, making it
less inclusive since most farmers are small-scale farmers. Therefore, the study recommends
that the government should increase the coverage of AES and prioritize small-scale farmers
to enhance the benefits of AE.
Acknowledgements: The authors wish to acknowledge the Institute for Research and
Community Service (LP2M) of the University of Jember for supporting this study. Also, we
wish to acknowledge the anonymous reviewer who provides constructive comments and
suggestions which significantly improve the original manuscript. The authors further
acknowledge the INFRARED Research Group for providing the data that was used in this
study.
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