CC BY
0JWPR
Journal of World's Poultry Research
2023, Scienceline Publication
J. World Poult. Res. 13(2): 223-232, June 25, 2023
Research Paper, PII: S2322455X2300025-13 License: CC BY 4.0
DOI: https://dx.doi.org/10.36380/jwpr.2023.25
Comparative Analysis of Contract Farming Effect on Technical Efficiency of Broiler Chicken Farms in
Indonesia
Efri Junaidi1, 2©, Jamhari1©, and Masyhuri1©
'Department of Agricultural Socio-Economics, Faculty of Agriculture, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia 2Bogor Agricultural Development Polytechnic, Agricultural Extension and Human Resource Development Agency, Ministry of Agriculture Republic
Indonesia, Cibalagung, Bogor 16001, Indonesia
Corresponding author's Email: efri.piliang@gmail.com
Received: 09 April 2023 Accepted: 28 May 2023
ABSTRACT
The development of broiler chicken farms in Indonesia has taken two forms, namely non-contract and contract farming. This study aimed to compare the technical efficiency levels of production in these two types of farming in Banten Province, Indonesia. Data were collected randomly from 180 broiler chicken farmers, consisting of 103 non-contract and 77 contract farmers. The study used the stochastic frontier production function to meet its objectives. The results showed that non-contract broiler chicken farmers were less efficient in their production than those under contract. The mean technical efficiency of the production factor for non-contract broiler chicken farmers was 0.689, ranging from 0.339 to 0.996. On the contrary, broiler chicken farmers under contract had a higher mean efficiency value of 0.893, ranging from 0.638 to 0.988. Moreover, the type of input supplier had a significant positive effect on technical inefficiency in non-contract farms. Non-contract farmers who purchased their production needs from a poultry shop showed higher technical efficiency compared to those who used distributors. This research sheds light on the efficiency of broiler chicken farms, both non-contract and contract, enabling all stakeholders, including the government, to devise appropriate policies for the development of broiler chicken farming. The study provided valuable insights into the technical efficiency levels of broiler chicken farming in Indonesia, which can help farmers identify areas that need improvement and develop strategies to increase productivity and profitability.
Keywords: Broiler farm, Contract farming, Input suppliers, Stochastic frontier, Technical efficiency
Contract farming has been proposed as an instrument to enhance farmer welfare. Contract farming is assessed to solve farmers' classic problems, such as capital limitation, technology mastery limitation, low productivity, and marketing problems. Many studies showed evidence that contract farming could increase farmer income and welfare (Otsuka et al., 2016; Ton et al., 2017; Bellemare
and Bloem, 2018) as well as their productivity (Reardon et al., 2009; Bellemare, 2010; Mishra et al., 2022). Most empirical studies about contract farming have indicated a positive and significant effect on farmer income (Otsuka et al., 2016). Contract farming might also increase farmer opportunities to adopt technology and invest more in technology (Mao et al., 2019).
Smallholder farming is a high-risk and vulnerable sector, susceptible to fluctuations in commercial broiler
To cite this paper: Junaidi E, Jamhari, and Masyhuri (2023). Comparative Analysis of Contract Farming Effect on Technical Efficiency of Broiler Chicken Farms in Indonesia. J. World Poult. Res., 13(2): 223-232. DOI: https://dx.doi.org/10.36380/jwpr.2023.25
chicken farming. Smallholder farmers are considered the weakest and most vulnerable group in the commercial broiler chicken business, compared to integrators and semi-integrators (Pambudy, 2013). This vulnerability is due to several factors, including limited knowledge and financial capacity to implement business management, intensive technology, and high biosecurity, smallholder farmers' heavy reliance on obtaining quality inputs, particularly day-old chicks, feed, and medicines; and their exposure to price fluctuations as price takers for both input and live chicken sale prices. These risk factors significantly affect the sustainability of smallholder farming businesses (Pambudy, 2013).
In the risky commercial broiler chicken farming environment, farmers are motivated to manage their farming activities efficiently. Efficient farming activities are closely related to decision-makers' ability to allocate resources efficiently to achieve maximum results (Ellis, 1993). One way to enhance efficiency and expand the scale of commercial broiler chicken farming is through contract farming, which presents a greater likelihood of increasing the scale of smallholder farming businesses (Wakhidati et al., 2018). According to Key and Runsten (1999), contract farming offers several advantages, including facilitating access to markets, credit, and technology, better risk management, and improved job opportunities for farmers. At the same time, core companies can benefit from reduced investment costs and can concentrate on gaining entry into modern and global markets.
The implementation of contract farming in broiler chicken agribusiness has existed for the last three decades alongside non-contract broiler chicken farms. Non-contract broiler chicken farmers are independent farmers who purchase production factors (inputs) and freely sell their output in the market. Production factor is an economic concept that refers to the inputs needed to produce goods and services. The purchase of production factors (inputs) is usually through production agencies, such as distributors, poultry shops, or other non-contract farmers. A contract farmer is a farmer who partners with other parties as a core company, such as an integrator or poultry shop, based on an agreement. In practice, broiler chicken contract farmers can be classified into three types, including profit-sharing contract farmers, makloon" contract farmers, and forward contract farmers. The first group involves smallholder farmers who buy inputs from the core company and sell the output to the core company or other parties with a core company permit, and the sale result is divided according to the agreement. "Makloon"
contract farmers are paid by the core company according to the number of day-old chicks (DOC) when starting the production, which is known as the management fee system. Forward contract farmers conduct production according to the contract agreement with the core company and sell the output to the core company with a fixed price stated in the contract (Amam et al., 2019; Indrawan et al., 2020).
Broiler chicken farming is mostly carried out by smallholder broiler chicken farmers in Indonesia. Smallholder farming in Ethiopia generally faces production efficiency problems (Yami et al., 2013; Mezgebo et al., 2021). Production efficiency is an important factor for broiler chicken farmers to increase their productivity, and it depends on how production factors are allocated. The present study aimed to compare the technical efficiency levels of non-contract and contract broiler chicken farming in Banten Province, Indonesia, to shed light on the potential benefits and drawbacks of each approach.
MATERIALS AND METHODS
Ethical approval
This research has been proposed and approved to be carried out by the Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Study design
This study was carried out in Banten Province, Indonesia, using a multistage sampling method. The research location was determined purposively based on the areas that are known for broiler chicken production. The selected regencies were in Tangerang Regency and Serang. The sub-districts in Tangerang Regency were Pakuhaji, Teluknaga, Kemiri, Rajeg, and Cisoka (BPS-Statistics Tangerang Regency, 2014), and Serang Regency was Kopo, Jawilan, Cikande, Pamarayan, and Cinangka (BPS-Statistics Serang Regency, 2014).
The data collection for this study was carried out between May and September of 2021, and the sample population included 324 smallholder broiler chicken farmers, comprising 185 non-contract farmers and 139 contract farmers, from 10 sub-districts. The sample size of 180 farmers was determined using the Slovin technique, and the sample was allocated proportionately to both non-contract and contract farmers, with 103 non-contract farmers and 77 contract farmers being selected. Respondents were randomly selected from each subdistrict based on the number of farmers. The data were
collected through direct interviews using a questionnaire and only one production period was considered for this cross-sectional study.
The data collected for broiler chicken farming pertained to both production factors (input) and production (output). Production data was measured in kilograms and represented the quantity of live birds harvested during a production period. Production factor data (input) included the number of day-old chicks (DOC), amount of starter and finisher feed, medicine and vitamins, vaccine, number of workers, and the broiler house area, all measured during a production period, and respectively expressed in head, kilograms, grams, milliliters, days, and square meters. Additionally, data was collected on the selling price of live birds per kg and the price of each input. Socio-economic data was also collected, including information on the farmers' age, education, experience, type of job, and input suppliers for non-contract farmers. For contract farmers, data was gathered on the type of integrator company they partnered with, whether it was an integrator or a poultry shop, along with details on the contract form (written or oral) and the contract type (forward contract or profit sharing/management fee).
Statistical analysis
This study employed stochastic frontier analysis using the Cobb-Douglas production function, which was transformed into natural logarithm form and estimated using Maximum Likelihood Estimation (MLE). The Frontier version 4.1 software was used for data processing. Stochastic frontier Cobb-Douglas production function for non-contract broiler chicken farmers and contract farmers were followed as Formula 1:
LnY = a0 + a1LnX1 + a2LnX2 + a3LnX3 + a4LnX4 + asLnXs + a6LnX6 + a7LnX7 + (vt — u1) (Formula 1)
Where Y, is the output of broiler chicken produced in one period (kg), X1 denotes the number of day-old chicks in one period (head), X2 determines the number of starter feed in one period (kg), X3 signifies the number of finisher feed in one period (kg), X4 refers to the number of medicines and vitamins in one period (g), Xs tabulates the number of vaccines in one period (ml), X6 is the number of labor in one period (days), X7 stands for the broiler chicken house area (m2), a0 presents constant, a1 — a7 are estimated parameters, (vt — u1) is error term (vt stands for disturbing effect and u1 determines inefficiency effect). The model is used to estimate the technical efficiency levels of broiler chicken farmers, with the aim of
identifying factors that contribute to differences in technical efficiency levels between contract and non-contract farmers.
The analysis of regression coefficients was used to assess the impact of input variables (x) on production. A positive regression coefficient indicates a positive effect of the variable x on production, while a negative regression coefficient implies a negative effect of x on production. The p-value reflects the probability that the observed effect of the variable x on production is due to chance. A p-value less than the significance levels that were p < 0.05 indicated that the impact of x on production was statistically significant, and p < 0.01 was highly significant.
Analysis of technical efficiency (TE) of broiler chicken farms used the method developed by Coelli et al. (1998). It was obtained from the ratio of the observation output of farmer-i^ (Yi) to the frontier output (Y*), which was followed as Formula 2:
TE:=^ = -Y-±-
1 Yj* exp(XiP)
exp(X,p,—ui) exp(XiP)
= exp(-Ui)
(Formula 2)
Where, TEi denotes technical efficiency of farmerith, Yi is output observed of farmer-i^, Y* signifies output frontier estimated, exp(-Ui) is expected mean of inefficiency effect (ui). Evaluation of technical efficiency value using the Criteria of 0 and 1. A technical efficiency value of 1 indicates that the farmer produces an optimal output and uses inputs efficiently. Conversely, a value of 0 suggests that the farmer is not achieving optimal output. For values of technical efficiency less than 1, it implies that the farmer has room for improvement in their production process. A comparative analysis was performed to determine the technical efficiency values of contract and non-contract farming. A two-sample mean-comparison test was employed to conduct the analysis, and the statistical software Stata version 12 was used.
Furthermore, an analysis of technical inefficiency effects and the factors that influence technical inefficiency was carried out using the technical inefficiency effect model from Coelli et al. (1998). The technical inefficiency effect model for non-contract and contract farms were followed as Formula 3:
ui=p0 + P1Z1 + p2Z2 + p3Z3 + P4Z4 + P5Z5 (Formula 3)
And the technical inefficiency effect model was followed as Formula 4:
ui=p0+ P1Z1 + P2Z2 + P3Z3 + P4Z4 + Pe^e +
p7Z7+p8Z8
(Formula 4)
Where Uj is the technical inefficiency effect, Z1 determines age of the farmer (years), Z2 presents education level (years), Z3 denotes the farming experience (years), Z4 is dummy variable of job type (1 = main job, 0 = side job), Z5 is a dummy variable of input suppliers, for non-contract farms only (1 = integrator, 0 = poultry shop/other farmers), Z6 is dummy variable of core company type (1 = integrator, 0 = poultry shop/other farmer), Z7is dummy variable of contract form (1= written, 0 = oral), Z8 presents dummy variable of contract type (1 = forward contract, 0 = profit sharing/management fee), p0 signifies constant, p1 — is an estimated parameter. Dummy variable is a binary variable that takes the values 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome. The coefficients estimated for each explanatory variable would indicate the direction and strength of their
impact on technical inefficiency. A statistically significant coefficient indicates evidence suggesting that the explanatory variable has a non-zero effect on technical inefficiency.
RESULTS AND DISCUSSION
Average cost production
The average production cost of broiler chicken farms for both non-contract and contract farms is shown in Table 1. The majority of broiler chicken production costs for both types of farmers was the cost of feed, which was more than 60% of the total production cost. The cost of feed for contract farmers was higher than for non-contract farmers. However, other variable production costs for contract farmers were lower, except for fuel, electricity, and litter.
Table 1. Average production cost of non-contract and contract broiler chicken farms in Banten Province, Indonesia
Variables Unit Non-contract farms (n=103) Contract farms (n=77)
Total cost Percentage Total cost Percentage
DOC IDR 24,542,143.69 30.47 93,268,571.43 26.10
Starter Feed IDR 26,784,500.00 33.26 72,823,431.82 20.38
Finisher Feed IDR 23,361,116.50 29.01 169,012,116.88 47.29
Medicine and vitamin IDR 636,533.98 0.79 1,095,137.66 0.31
Vaccine IDR 665,756.10 0.83 572,796.43 0.16
Labor IDR 2,019,478.65 2.51 6,009,713.94 1.68
Fuel, electricity, litter IDR 1,840,744.34 2.29 11,326,466.91 3.17
Depreciation of broiler chicken house IDR 475,383.94 0.59 1,988,538.45 0.56
Depreciation of equipment IDR 208,371.87 0.26 1,283,700.16 0.36
Total cost IDR 80,534,029.07 100.00 357,380,473.68 100.00
Source: Primary data analysis (2021), n: Number of samples, DOC: Day old chick
Descriptive statistics of production and inputs
Table 2 presents the descriptive statistics of the variables used to analyze stochastic frontier Cobb-Douglas production. The results showed that contract farmers employed significantly higher amounts of production inputs than non-contract farmers, which resulted in higher levels of output for contract farmers (19,440.77 kg) than non-contract farmers (3,929.92 kg). On average, both groups of farmers were above 40 years of age, had 9 years of education, and possessed extensive experience in broiler chicken farming (average of 9 and 11 years for non-contract and contract farmers, respectively).
Additionally, broiler chicken farming was the main occupation for both groups of farmers. Most non-contract farmers obtained their production inputs from distributors, while the majority of contract farmers cooperated with integrators and operated under written contracts with a profit-sharing/management fee. These findings provide valuable insights into the differences in production practices between contract and non-contract broiler chicken farmers and underscore the importance of examining the impact of these differences on technical efficiency levels.
Table 2. Descriptive statistics of the variables used in Stochastic Frontier Cobb-Douglas production function for non-contract and contract broiler chicken farms in Banten Province, Indonesia
Non-contract farms Contract farms
Variables (n = 103)_(n = 77)
Mean SD Mean SD
Production kg 3929.92 7940.64 19440.77 50226.27
DOC Heads 3165.70 6377.21 11832.47 24614.03
Starter Feed kg 3330.26 4980.87 8545.70 16657.57
Finisher Feed kg 3020.29 9981.15 20323.61 62946.18
Medicine and vitamin (g) g 980.49 1907.36 3586.88 8451.14
Vaccine ml 210.63 636.63 1515.71 6428.12
Labor Days 21.19 33.83 81.45 201.82
Broiler chicken house area m2 347.58 533.32 1023.23 1730.94
Age Years 41.79 10.10 45.94 10.65
Education Years 9.08 3.75 9.77 3.20
Experience Years 9.93 5.96 11.19 6.48
Type of Job (1 = main job, 0 = side job) Dummy 0.60 0.49 0.69 0.47
Input suppliers (1 = distributor, 0 = poultry shop) Dummy 0.43 0.50 - -
Core company (1 = integrator, 0 = poultry shop/other farmer) Dummy - - 0.58 0.49
Contract form (1 = written, 0 = oral)) Dummy - - 0.60 0.49
Contract type (1 = forward contract, 0 = profit sharing/management fee) Dummy - - 0.43 0.50
Source: Primary data analysis (2021), n: Number of samples, DOC: Day old chick, SD: Standard deviation
The estimation of the stochastic frontier production function
The production function consists of two types of variables, namely independent and dependent variables. The study included several independent variables, namely the number of DOC, starter feed, finisher feed, medicine and vitamins, vaccines, labor, and broiler chicken house area. The dependent variable was the output of the broiler chicken produced. The MLE results of the stochastic frontier Cobb-Douglas production function are presented in Table 3.
The findings revealed that DOC was a significant input for non-contract and contract farmers (p < 0.01) and estimated elasticities of 0.9061% and 1.0170%, respectively, indicating its significant impact on broiler chicken production. Similarly, starter and the finisher feeds were significant (p < 0.05) for non-contract farmers, with elasticities of 0.0576 Kg and 0.0176 Kg, respectively. For contract farmers, finisher feed and vaccines were significant with elasticities of 0.0183% and 0.0163%, respectively (p < 0.05). Increasing the use of these inputs has a positive and significant impact on broiler chicken production.
For non-contract and contract farmers, DOC costs contributed 30.62% and 26.12% to production costs, respectively. The results of this study were in line with the research by Harianto et al. (2019) and Ullah et al. (2019).
Ullah (2019) found the estimated coefficient of the amount of DOC was statistically significant. Harianto et al. (2019) also stated that DOC was the major driver for broiler chicken production.
The study also highlighted the significance of starter feed and finisher feed for non-contract farmers (p < 0.05). The use of these inputs could lead to a significant increase in production. Previous studies have also reported similar findings, demonstrating the positive effect of feed on production (Udoh and Etim, 2009; Pramita et al., 2018; Wantasen et al., 2021). The present study provides further evidence supporting the importance of feed as a significant input in the production process for both non-contract and contract farmers. The findings underscore the need for farmers to exercise meticulous control over their feed inputs to maximize their production output. In the other hand, starter feed and finisher feed were not significant input for contract farmers (p > 0.05). This is due to the fact that feed usage in contract-based broiler chicken farming is determined by the core company.
The feed was categorized into starter and finisher with regard to the type. Starter feed is the type of feed given to the broiler chicken at the age of 2-4 weeks. In contrast, finisher feed is given to the broiler chicken at 4-6 weeks. Production cost for these types of feeds accounted for more than 60% of the total cost of production. The same result was shown by Adeyonu and Odozi (2022),
indicating feeding is the primary factor responsible for elevating the broiler chicken production costs by approximately 75% of the variable cost.
Vaccination is a commonly used method by farmers to enhance the immunity of broiler chickens and reduce their susceptibility to diseases. The current results indicated that vaccines significantly and positively affected the production of contract farmers. These positive and significant coefficients suggested that increasing the use of vaccines could lead to higher broiler chicken production (p < 0.05). This finding is consistent with previous research by Harianto et al. (2019), indicating the positive effect of vaccines on broiler chicken production. In contrast, the study revealed that the coefficient for the use of vaccines was negative and statistically nonsignificant for non-contract farmers. This suggests that the quantity of vaccines used by non-contract farmers was consistent across all farmers, as observed in previous research by Ullah et al. (2019).
This study was designed to estimate the value of the gamma parameter (y) in non-contract and contract broiler chicken farms. The estimated value of y in non-contract farms was 0.9878, which was statistically significant (p < 0.01). This indicates that 99.99% of the residual variation in the model was due to technical inefficiencies that farmers can control, while the remaining 0.01% was due to stochastic effects (vt). The high value of y implies that non-contract farmers had a high degree of control over
their production processes, and that any inefficiencies can be mitigated by improvements in technical efficiency. On the other hand, the estimated value of y in contract broiler chicken farms was 0.3777%, which was not significant at p < 0.01 but statistically significant at p < 0.05. This implies that 37.77% of the residual variation in the model was due to technical inefficiencies that farmers can control, while the remaining 62.23% was due to stochastic effects (vi). The lower value of y in contract farms compared to non-contract farms suggests that farmers in contract farms have less control over their production processes, and are more vulnerable to external factors that affect production efficiency. Overall, the estimated values of y in both non-contract and contract broiler chicken farms suggest that there is room for improvement in the technical efficiency of broiler chicken production.
The Likelihood Ratio test values for non-contract and contract farms were 19.1553 and 27.3582, respectively. Both values were greater than the critical value of x2 at a = 0.01, as presented in Kodde and Palm's table (1986), which was 17.755 and 20.972, respectively. The LR test values were highly significant at p < 0.01, suggesting that technical efficiency and technical inefficiency factors significantly impact broiler chicken production. This implies that the combined inefficiency variables in the inefficiency effect model contribute to technical inefficiency in the broiler chicken production process.
Table 3. Estimates of the stochastic frontier production function of non-contract and contract broiler chicken farms in Banten Province, Indonesia
Unit Non-contract farms (n=103) Contract farms (n=77) Variables -
Coefficient T-ratio Coefficient T-ratio
Constant Kg 0.6949** 2.5383 -0.0234 -0.1132
DOC Heads 0.9061*** 19.1075 1.0170*** 17.2030
Starter Feed Kg 0.0576** 2.2752 0.0347 1.0040
Finisher Feed Kg 0.0176** 2.1055 0.0183 1.7578
Medicine and vitamin g -0.0117 1.1938 -0.0149 -0.6845
Vaccine ml -0.0030 -0.3947 0.0163** 2.0693
Labor Days 0.0503 1.1690 -0.0110 -0.3078
Broiler chicken house Area m2 0.0056 0.1520 -0.0067 -0.1359
Sigma-squared 0.0327*** 7.2688 0.0362*** 3.9777
Gamma 0.9999*** 71.2554 0.3777** 2.2774
Log likelihood function 33.7219 28.7530
LR test 19.1553*** 27.3582***
Source: Primary data analysis (2021), ** p < 0.05, *** p < 0.01, n: Number of samples, LR test: Likelihood ratio test
The value of the technical efficiency of broiler chicken farmers is presented in Table 4. The technical efficiency value of non-contracted broiler chicken farmers ranges from 0.339 to 0.996, with a mean value of 0.689. The average efficiency value means that the average non-contracted broiler chicken farmer could only achieve a production of 68.9% of the production input that has been used. This implies that the opportunity for non-contracted broiler chicken farmers to increase output is still very large (31.1%) in case they want to be technically efficient and achieve frontier output. Meanwhile, the mean efficiency of contract farms was 0.893, with the lowest value of 0.638 and the highest of 0.988. The average efficiency value showed that contract broiler chicken farmers achieved production of 89.3% of all production inputs used. The implication is that contract broiler chicken farmers still have the opportunity to increase output by 10.7% to be technically efficient and achieve frontier output.
Based on the distribution of technical efficiency value, 56.31% of non-contract farmers with an efficiency value below 0.70, 33.98% of non-contract farmers had technical efficiency of 0.70 to 0.89, and 9.71% of non-contract farmers had technical efficiency above 0.90. Meanwhile, the technical efficiency value of contract farmers below 0.70 was 2.60% of contract farmers, between 0.70 and 0.89 was 42.86%, and the majority of broiler chicken farmers (54.55%) had a technical efficiency above 0.90. The results showed that farmers under contract had greater technical efficiency than non-contract farmers in Banten province. The comparative test results confirmed a significant difference in the level of technical efficiency between non-contract and contract farmers.
Non-contract farmers could be classified as low efficiency because the average value of technical efficiency was only 0.689. It means that efficiency is still low and can still be increased. The low value of mean technical efficiency (TE = 0.6803) was also found in non-contract farming in Nigeria (Adebisi et al., 2020). Meanwhile, contract farmers were categorized as highly efficient because they have a mean technical efficiency value of 0.893 (close to 1). The average value of TE was lower than the average value of TE found in the other studies that are higher than 0.90 (Ullah et al., 2019; Bana et al., 2021; Zimunya and Dube, 2021).
Several studies have indicated that contract farming can increase efficiency (Begum et al., 2012, Suwarta, 2012; Harianto et al., 2019). Begum et al. (2012) found that contract farming had a positive and significant effect
on the technical efficiency of poultry farms. Suwarta (2012) investigated the efficiency of broiler chicken farms under the core company integrator and independent broiler chicken farms in Sleman Regency. The study results indicated that farmers under contract were more technically efficient than non-contract farmers. Furthermore, the technical efficiency of broiler chicken farms under contract with the core company integrator was higher than those under contract with an independent broiler chicken farm as the core. Harianto et al. (2019) revealed that broiler chicken farms under written formal and detailed contracts had better technical efficiency than those under non-formal and unwritten contracts. Moreover, contract farming could increase the productivity of broiler chicken farms, as contract farmers have better production performance than non-contract farmers (Bahari et al., 2012; Majid and Hassan, 2014; Saptana et al., 2017). However, a study by Bana et al. (2021) compared contract and non-contract broiler chicken farms in Kupang, East Nusa Tenggara, Indonesia. The result indicated that although both types of farming were technically efficient, contract farming was less efficient than non-contract farming.
The results of the comparative test of technical efficiency levels between contract and non-contract broiler chicken farmers are presented in Table 5. The p < 0.01 indicated a significant difference in technical efficiency levels between contract and non-contract farmers. These results indicated a significant difference in the level of technical efficiency between contract and non-contract farmers, with contract farmers exhibiting higher levels of technical efficiency. The finding of this study supported the argument that contract farming could improve the technical efficiency levels of broiler chicken farmers. This may be due to the support and guidance provided by the corporations, which enables contract farmers to access better inputs, such as feed and vaccines, and to implement more efficient farming practices. Non-contract farmers, on the other hand, may face challenges in accessing these resources and may lack the necessary knowledge and skills to optimize their production processes.
Table 6 presents the estimated technical inefficiency effect model of broiler chicken production. This study has identified a significant variable impacting technical inefficiency among non-contracted and contracted farmers. For non-contracted farmers, input suppliers were significant (p < 0.01). The input supplier variable has a positive coefficient value, indicating that buying inputs from an integrator through a distributor will cause
technical inefficiency to increase for non-contract farmers. However, purchasing inputs from a poultry shop can improve technical efficiency for non-contract farmers. These findings are significant for non-contract broiler chicken farmers. The results focus on the importance of carefully selecting input suppliers and utilizing experience to enhance technical efficiency and improve production outcomes.
Experience was a significant variable for contracted farmers (p < 0.01). Experience was the variable that impacts technical inefficiency in contracted farmers. The positive coefficient sign for contracted farmers indicates that more experienced farmers will increase technical inefficiency or become less efficient. This shows that technical inefficiency will increase in line with the increase in experience. The observed positive effect of
experience on technical inefficiency can be attributed to the fact that contract farmers work according to the instructions of the core company, limiting the role of experience in improving efficiency.
Compared to non-contract farms, the efficiency of contract farms can be attributed to the involvement of the core company in the production process. Although in the current study, the variables of core company type, contract form, and contract type were not significant (p > 0.05), the core company plays a role in providing inputs and offering technical guidance on production, enabling contract farmers to understand resource allocation better and allocate them more efficiently. This was found in studies conducted by Eaton and Sheperd (2001), Simmons (2002), Bellemare et al. (2013), and Cahyadi and Waibel (2016).
Table 4. Technical efficiency level of non-contract and contract broiler chicken farms in Banten Province, Indonesia
Level of technical efficiency
Non-contract farms (n=103)
Contract farms (n=77)
Total
Percentage
Total
Percentage
< 0.70 58.00 56.31 2.00 2.60
0.70 - 0.79 28.00 27.18 16.00 20.78
0.80 - 0.89 7.00 6.80 17.00 22.08
> 0.90 10.00 9.71 42.00 54.55
Total 103.00 100.00 77.00 100.00
Maximum 0.996 0.988
Minimum 0.339 0.638
Average 0.689 0.893
Source: Primary data analysis (2021), n: Number of samples
Table 5. Comparative test results of technical efficiency level of non-contract and contract broiler chicken farms in Banten
Province, Indonesia_
Variables
Non-contract farms (n=103)
Contract farms (n=77)
Mean
SD
Mean
SD
T-ratio Significant
Technical efficiency
0.6887
0.1321
0.8933
0.1003
-11.8114
0.0001
Source: Primary data analysis (2021), p < 0.01, n: Number of samples
Table 6. Estimates of technical inefficiency of non-contract and contract broiler chicken farms in Banten Province, Indonesia
Variables Units Non-contract farms Contract farms
(n=103) (n=77)
Coefficient T-ratio Coefficient T-ratio
Constant 0.3833 1.8677 0.1180 0.3257
Age Years 0.0002 0.1011 -0.0031 -0.5584
Education Years 0.0003 0.0584 -0.0030 -0.1952
Experience Years -0.0080 1.9634 0.0217*** 2.4704
Type of Job (1 = main job, 0 = side job) Dummy 0.0159 0.3942 -0.0287 -0.2565
Input Suppliers (1 = distributor, 0 = poultry shop) Dummy 0.1529*** 3.3857 - -
Core company (1 = integrator, 0 = poultry shop/other farmer) Dummy - - -0.0872 -0.5141
Contract form (1 = written, 0 = oral) Dummy - - 0.0526 0.3298
Contract type (1 = forward contract, 0 = profit sharing/management fee) Dummy - - -0.0900 1.7560
Source: Primary data analysis (2021), p < 0.01, n: Number of samples, T-ratio: T-value or T-statistic
CONCLUSION
Non-contract broiler chicken farms were less technically efficient than those under contract. The mean value of technical efficiency on non-contract broiler chicken farmers was 0.689 ranging from 0.339 to 0.996. Meanwhile, broiler chicken farmers under contract had a higher mean efficiency value of 0.893, with the lowest value of 0.638 and the highest of 0.988. The input suppliers' type had a positive and significant effect on technical inefficiency in non-contract farming, where buying input from a poultry shop increases technical efficiency compared to buying input from the distributor. Technical efficiency improvements in production need to be made by contract and non-contract farmers through improved production management. In addition, the availability of production inputs and ease of access to inputs can support farmers' technical efficiency improvements. The findings could have important implications for broiler chicken farmers as the study highlights the critical role of inputs, such as DOC and feed in the production process. Farmers should ensure that these inputs are readily available as their optimal utilization positively affects production outcomes. Government support through regulation is crucial to ensure the availability of inputs and ease of access for farmers. Future research is related to the input market structure, distribution of the input supply to farmers and strategy for selecting input suppliers and core companies.
DECLARATION
Acknowledgments
This research was funded by the Ministry of Agriculture Republic Indonesia through the scholarships program of the Agricultural Extension and Human Resource Development Agency.
Authors' contributions
Efri Junaidi contributed to data collecting, data analysis, analysis of the results, and preparing the manuscript. Jamhari and Masyhuri contributed to the design and supervision of the research, analysis of the results, and revised the manuscript. All authors have checked and approved the final version of the manuscript for publication in the present journal.
Competing interests
The authors confirm that there was no conflict of interest with any financial, personal, or other relationships with other people or organizations related to this paper.
Ethical consideration
Before publication in the present journal, the authors have checked the ethical issues, including plagiarism, consent to publish, misconduct, data fabrication and/or falsification, double publication and/or submission, and redundancy.
Availability of data and materials
The data presented in this study are available on request from the corresponding author.
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