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INTRODUCTION
Horticultural crops play a critical role in human life due to their nutritional value and contribution to food security (Rajani and Shourabh, 2017; Amane, 2023). Horticulture is one of the sub-sectors of agriculture that significantly contributes to poverty alleviation, public welfare and Indonesia's economy (Choudhary, 2013; Pitaloka, 2017). It includes a wide range of crops, such as vegetables, fruits, ornamental plants, and bio-pharmaceuticals. Among the prominent horticultural commodities in agribusiness is the tomato (Solanum lycopersicum L.), which provides the required source of nutrients. Tomato is a vital source of vitamins C and A, minerals, and carotene, all essential to human health (Ullah et al., 2016; Akotowanou et al., 2022; Sinaga, 2023). The demand for fresh and processed tomatoes has been rising consistently due to the increasing need for balanced nutrition. Tomatoes are not only valuable as vegetables but also as a fresh food source that can address vitamin deficiencies. In addition, tomato farming demonstrates high productivity potential (Wowiling et al., 2023).
Tomato productivity is closely linked to the success of agricultural practices, which can be measured through yield outcomes, income generation, and operational efficiency (Dyanto et al., 2022). Tomato farming has proven to be highly productive, with an average yield of 16.6 t ha-1, and is considered very profitable, with an efficiency ratio of 2.5 (Akbarrizki, 2017; Mahyudi and Husinsyah, 2019; Dyanto et al., 2022). Tomato production in Indonesia has experienced continuous growth over the years. Data from the Indonesian Central Bureau of Statistics (CBS) reveal that tomato production reached 1.11 million tons in 2021, a 2.72% increase from the previous year’s 1.08 million tons. Since 2017, national tomato production has trended upward, reaching its highest level in the last decade in 2021. In 2018, production was recorded at 976,772 t, increasing to 1,020,331 t in 2019, 1,084,993 t in 2020, and 1,107,575 t in 2021 (Badan Pusat Statistik, 2022).
Tombolo Pao District in Gowa Regency is a region with high potential for horticultural development, particularly in tomato farming. Tomatoes are cultivated throughout the district, which benefits from favorable climatic conditions (Safir et al., 2023). However, farmers in this area typically engage in cultivation without considering the associated costs, leading to challenges related to production factors that could influence farm outcomes. This situation underlines the importance of understanding how production factors impact farming efficiency. Farmers must be aware of the critical production inputs that affect their agricultural practices and the costs associated with these inputs. Optimizing the use of production factors is one of the key strategies to increase tomato yields and ensure that farming activities are efficient.
Tomato production efficiency depends heavily on the effective use of inputs such as land, seeds, fertilizer, pesticides, and labor. Inefficient use of these inputs can lead to reduced productivity and profitability. The literature highlights various factors influencing tomato production, including land size, seed quality, and input costs (Degefa et al., 2023; Kartika and Kurniasih, 2021). Inefficient farming practices have been attributed to the misallocation or overuse of key inputs such as fertilizers, resulting in diminished returns and environmental sustainability (Ren et al., 2021). For instance, improper timing or excessive use of fertilizers can harm soil fertility, reducing crop yields rather than enhancing them (Setyorini et al., 2006). Similarly, improper application of pesticides can reduce crop protection efficacy, leading to lower overall production levels (Hadi and Sita, 2016).
The research problem at the core of this study centers on inefficiencies in the use of production inputs in tomato farming in Tombolo Pao District. The general solution to this problem, based on the literature, is to optimize input use to ensure that each production factor contributes maximally to farm output. In particular, previous studies suggest that balancing the use of land, seeds, fertilizers, and labor is key to achieving higher productivity and efficiency in tomato farming (Chiarella et al., 2023). As demonstrated in earlier research, farm efficiency can be improved through the adoption of improved agricultural practices, better seed varieties, and more effective input management (Sri, 2016; Hadi and Sita, 2016; Ren et al., 2019).
Specific solutions from the literature focus on several key areas. First, increasing the efficiency of land use is essential, as larger, better-managed plots tend to yield higher outputs (Aragón et al., 2022; Ma et al., 2023). Efficient land use involves not only maximizing the area under cultivation but also ensuring that the soil is adequately prepared and maintained for optimal productivity. Another key area is seed selection. Research shows that using high-quality seeds significantly impacts tomato production (Özer, 2018; Gallegos-Cedillo et al., 2024). Seed varieties that are more resistant to pests and diseases, combined with proper planting techniques, can lead to substantial improvements in yield. Additionally, managing fertilizer application is critical, as excessive or poorly timed fertilization can lead to nutrient imbalances and phosphorus loss that adversely affect crop growth and yields (Liu et al., 2021; Noulas et al., 2023). Labor efficiency also plays an important role; highly skilled workers who apply best practices in crop management can significantly boost tomato productivity (Chiarella et al., 2023).
Despite these insights, several gaps remain in understanding how best to optimize production inputs in the specific context of highland tomato farming in Indonesia. While previous studies have identified general input efficiency issues, few have focused specifically on the stochastic nature of agricultural production in highland areas. Additionally, the unique climatic and geographical factors of highlands require a tailored approach to input management in developing tomato farming businesses (Mario et al., 2005).
This study aims to fill these gaps by analyzing the efficiency of tomato farming in the Highlands Gowa Regency using a stochastic frontier analysis. The research focuses on identifying the production factors that have the greatest impact on output and assessing the degree of efficiency with which these inputs are used by farmers. The novelty of this study lies in its application of a stochastic frontier model to measure technical, price and economic efficiency in highland tomato farming, an approach that has not been widely employed in previous studies. By evaluating how well land, seeds, fertilizers, pesticides, and labor are being utilized, this research seeks to provide a more comprehensive understanding of farming efficiency in the highlands.
Furthermore, this study will contribute to the literature by offering empirical insights that can guide agricultural policies aimed at improving farming practices in highland areas. The findings are expected to provide actionable recommendations for farmers and policymakers, helping to enhance productivity, profitability, and sustainability in tomato farming. The scope of this research extends to evaluating the technical efficiency of tomato production, as well as exploring the potential for optimizing the use of key inputs to achieve higher levels of efficiency.
MATERIALS AND METHODS
This research was conducted in Balassuka Village, Tombolo Pao District, Gowa Regency, from August 23 to October 8, 2023. This location was chosen because it is an area with an altitude of 600 m above sea level and an average rainfall of 100 mm-160 mm per year, which is suitable for planting tomatoes. The study aimed to assess the efficiency of tomato farming by examining the use of production factors including land area, seeds, fertilizer, pesticides and labor. The methodological approach adopted in this research was a structured and systematic process involving several key steps, ensuring that the findings were robust and reliable. The study employed a simple random sampling method to select the sample population of tomato farmers in Balassuka Village, Tombolo Pao. The population of tomato farmers in the village consisted of 233 individuals, and the sample size was determined using the Slovin formula (Tejada and Punzalan, 2012; Adam, 2020). The formula (1) is presented as follows:
where, N was total population and e margin of error (15%)
substituting the values:
Thus, a total of 60 tomato farmers were selected as the sample for the study. This sample size was considered representative of the population and sufficient for the statistical analyses to be performed. The study relied on both primary and secondary data sources. Primary data were collected through direct observation and structured interviews using a pre-prepared questionnaire. The questionnaire aimed to capture relevant data on the farmers' use of inputs such as land, seeds, fertilizer, pesticides, and labor. Interviews were conducted face-to-face with the selected farmers to ensure the accuracy and comprehensiveness of the data collected. In addition to primary data, secondary data were also utilized. These secondary sources included previous studies, government reports, and agricultural statistics from the Central Bureau of Statistics (CBS), which provided background information on tomato production trends in Indonesia and the region.
The primary objective of the data analysis was to identify the key production factors influencing tomato farming and to assess the technical and allocative efficiency of these factors. The Cobb-Douglas production function was used to model the relationship between tomato production (output) and the factors of production (input). The general form of the Cobb-Douglas production function is (2):
where, Y was tomato production (kg), X 1 land area (ha), X 2 = seed quantity (g), X 3 fertilizer use (kg), X 4 pesticide use (L), X 5 = labor (h), a constant, u error term, and b1, b2, b3, b4, b5, and b6 = coefficients representing the elasticity of each input with respect to output.
To simplify the model, the Cobb-Douglas function was transformed into a natural logarithmic form, which allowed for linear regression analysis (3)
This model enabled the estimation of the marginal contributions of each input to tomato production. The significance of each input in the model was tested using the t-test, calculated as follows (Soekartawi, 1993) (4)
where, bi was the coefficient of the independent variable and S bi standard error of the estimated coefficient.
The null hypothesis (H0) posits that the independent variable has no significant effect on the dependent variable (tomato production), while the alternative hypothesis (H1) suggests that the independent variable does have a significant effect. The decision rule is:
If t calculated > t table , reject H0 and accept H1, indicating that the variable has a significant effect on tomato production
If t calculated < t table , accept H0, indicating that the variable has no significant effect.
To assess the efficiency of input use in tomato farming, the study applied technical and allocative efficiency analysis. Technical efficiency was evaluated using a stochastic frontier production function, which accounts for random shocks and inefficiencies that may affect production output. For allocative efficiency, the study examined the marginal product value (NPMx) in relation to the input price (Px). Allocative efficiency is achieved when the marginal product value equals the input price, i.e., NPMx/Px=1. The formulas used in this analysis are as follows (5, 6 and 7)
where, NPMx was marginal product value of the input, PMx i marginal product of the input, bi elasticity of the production factor, x i average use of the input, Px price of the input, and Py price of output (tomato).
The criteria for evaluating allocative efficiency were PMx/Px<1 input is used inefficiently and should be reduced, PMx/Px>1 input is underutilized and should be increased, and PMx/ Px=1 input is used efficiently, and profit is maximized.
Finally, the study assessed economic efficiency, which combines technical and allocative efficiency. Economic efficiency (EE) was calculated using the following formula (8)
where, ET was technical efficiency and EH allocative efficiency.
The evaluation criteria for economic efficiency were EE<1 the farm is economically inefficient, EE=1 the farm is economically efficient, and EE>1 the farm is not yet economically efficient.
RESULTS AND DISCUSSION
Socio-economic characteristics of tomato farmers
The profile of respondents involved in this study provides a comprehensive understanding of the status of tomato farmers in the highlands of Tombolo Pao District, Gowa Regency. This section discusses the demographic characteristics of the respondents, including age, education level, farming experience, and land size. Additionally. The socio-economic characteristics of tomato farmers in the study area are presented in table 1.
Table 1. Socio-economic characteristics of tomato farmers.
| Variable | Association | Frequency | Percentage (%) |
|---|---|---|---|
| Age in years | 25-31 | 7 | 13.33 |
| 32-38 | 4 | 6.67 | |
| 39-45 | 15 | 25.00 | |
| 46-52 | 13 | 21.67 | |
| 53-59 | 10 | 15.00 | |
| 60-66 | 11 | 18.33 | |
| Totals | 60 | 100 | |
| Education level | Primary school | 17 | 28.33 |
| Secondary school | 9 | 15.00 | |
| High school | 21 | 35.00 | |
| Bachelor’s degree | 13 | 21.67 | |
| Totals | 60 | 100 | |
| Farming experience | 5-9 | 6 | 10.00 |
| 10-14 | 16 | 26.67 | |
| 15-19 | 14 | 23.33 | |
| 20-24 | 9 | 15.00 | |
| 25-29 | 10 | 16.67 | |
| 30-34 | 5 | 8.33 | |
| Totals | 60 | 100 | |
| Land size | 0.3-0.58 | 14 | 23.33 |
| 0.59-0.87 | 10 | 16.67 | |
| 0.88-1.16 | 2 | 3.33 | |
| 1.17-1.45 | 15 | 25.00 | |
| 1.46-1.74 | 13 | 21.67 | |
| 1.75-2.03 | 6 | 10.00 | |
| Totals | 60 | 100 |
Age of tomato farmers
Age is an essential factor influencing farming activities, as it directly impacts the farmer's physical ability, decision-making, and openness to adopting new farming technologies (Arita et al., 2022). Table 1 presents the age distribution of tomato farmers in Tombolo Pao District. Table 1 reveals that most tomato farmers are between 39 and 45 years old, accounting for 25% of the respondents, while the youngest group, aged 25 to 31 years, constitutes 13.33%. This indicates that the majority of farmers are still within the productive age range, allowing them to be physically capable and more likely to adopt new innovations in farming (Alam et al., 2016; Managanta, 2020). As a result, these farmers are expected to enhance tomato production through improved skills and knowledge.
Education level of the tomato farmers
Education plays a crucial role in agricultural practices, as it influences farmers' ability to comprehend and apply modern farming technologies, ultimately improving productivity. Table 1 shows the education levels of tomato farmers. It indicates that most respondents have attained a high school education (35%), followed by those with an elementary school education (28.33%). Education is an essential indicator of farmers’ capacity to manage technology and innovations; those with higher education levels, such as high school and bachelor’s degree holders, are expected to achieve better productivity compared to those with lower education levels.
Farming experience
Experience in farming is a key factor in determining a farmer’s ability to manage agricultural practices and address challenges. Table 1 presents the respondents' farming experience. The majority of farmers have 10-14 years of experience (26.67%), while only 8.33% have 30-34 years of experience. Farmers with more experience tend to have developed the skills necessary to enhance productivity and manage agricultural risks.
Land size
Land size is a critical production factor in agriculture, as larger land areas generally lead to higher yields. Table 1 provides an overview of the land size owned by the respondents. Most farmers (25%) own land between 1.17 and 1.45 ha, while only 3.33% own between 0.88 and 1.16 ha. The larger the land area, the greater the potential tomato yield, underscoring the importance of land size in agricultural productivity.
Factors influencing tomato farming
This section presents the results of the stochastic frontier analysis applied to estimate the impact of various production factors on tomato farming in Tombolo Pao District, Gowa Regency. The analysis is based on the Cobb-Douglas production function, and table 2 summarizes the findings for each production variable. Studies have shown that farms that optimize their input usage can significantly improve production efficiency, which is essential for agricultural growth (Frangu et al., 2018).
Table 2. Estimation results of tomato farming production factors.
| Variable | Coefficient | Standard error | t-ratio |
|---|---|---|---|
| Production | 0.20927823 | 0.33438997 | 0.24201629 |
| Ln X1 (land) | 0.39187047 | 0.17841825 | 2.1963586 |
| Ln X2 (seeds) | 0.36392189 | 0.18391427 | 1.9787583 |
| Ln X3 (fertilizer) | -0.91620249 | 0.41882314 | -2.1875642 |
| Ln X4 (pesticide) | 0.28843663 | 0.29828452 | 0.96698491 |
| Ln X5 (labor) | 0.50747247 | 0.42540827 | 0.11929069 |
The coefficients and t-ratios are used to determine the significance and influence of each production factor on tomato yields in the region. The findings for each variable are discussed below:
The influence of land area on tomato production
The coefficient for land is positive and statistically significant with a t-ratio of 2.19, which exceeds the critical value of 1.67. This indicates that land size significantly influences tomato production in Tombolo Pao. These results align with prior research (Weldegiorgis et al., 2018; Wadu, 2023), which suggests that land is a critical agricultural production factor that plays a substantial role in determining output. Larger land areas typically lead to higher production, while cropping area and fertile soil further enhance the productivity of the land for tomato farming.
The influence of seeds on tomato production
The seeds variable also exhibits a significant positive effect on tomato production, with a t-ratio of 1.97, indicating a substantial contribution to yield. This finding emphasizes the importance of using high-quality seeds, as confirmed by the results of research conducted by Misra et al. (2023), which explains that quality seeds are selected from high-yielding varieties characterized by disease resistance, and fast growth, helping farmers get better yields. However, the result contrasts with Majid et al. (2022), who found that seed use had no significant effect on production, likely due to overuse or poor-quality seeds in other regions. The findings in Tombolo Pao suggest that the use of improved seed varieties can substantially increase yields (Taiy et al., 2017; Pattiasina et al., 2023), provided they are applied in optimal quantities. The average cost of tomato seeds in the village is 25,000 IDR, which aligns with market conditions. The price of seeds is determined by the quality of seeds used by tomato farmers.
The influence of fertilizer on tomato production
The results show that fertilizer has a significant negative impact on tomato production, with a t-ratio of -2.18. This suggests that excessive or improper use of fertilizers may reduce productivity, a finding consistent with the work of Setyorini et al. (2006). Fertilizer application in tomato farming must be precise, both in terms of timing and quantity, to ensure the soil retains adequate nutrients. In Gowa Regency, over-fertilization may have led to soil nutrient imbalances, ultimately reducing the effectiveness of this input. The most commonly used fertilizers in the region include organic manure, urea, and phonska. The types and dosage of fertilizers used in tomato farming in Tombolo Pao Subdistrict, Gowa Regency are presented table 3.
The influence of pesticides on tomato production
The pesticide variable does not show a significant effect on production, with a t-ratio of 0.96, which is below the critical threshold of 1.67. This result aligns with the findings of Asfaw (2021), which suggest that pesticides, while having a positive effect, are often not applied in optimal quantities or are of inadequate quality to significantly impact production. Farmers commonly use chemical pesticides such as bemolik, and victory, which are readily available and affordable. However, the lack of proper guidance on pesticide use may be limiting its effectiveness.
The influence of labor on tomato production
Labor is another variable that does not significantly affect tomato production, with a t-ratio of 0.11. This result is consistent with Kurniawan et al. (2018), who found that labor, while essential, does not always translate into higher production when not managed effectively. Field observations show that the labor force working on tomato farms is dominated by unskilled workers. This is due to the lack of capital to hire skilled labor. Additionally, the lower education levels among farmers leads to inefficient labor use, further hindering productivity. Labor remains a crucial factor in various stages of tomato farming (Testa et al., 2014), including land preparation, planting, maintenance, and harvesting, but requires better management to contribute effectively to increased yields.
Tomato farming efficiency
The efficiency of tomato farming was analyzed to determine whether the combination of production factors was optimized for maximum output. The application of SFA in tomato farming efficiency analysis allows for a comprehensive understanding of both technical and allocative efficiency. Aparicio et al. (2023) and Thomas et al. (2024) classified efficiency into technical, allocative, and economic categories, providing a framework for assessing the overall performance of tomato farmers. This classification is essential for identifying specific areas where improvements can be made, such as optimizing input usage or enhancing management practices. This section presents the findings on technical, price, and economic efficiency, based on the data collected from farmers in the region.
Technical efficiency
Technical efficiency refers to how effectively production factors are utilized in the farming process. The technical efficiency of tomato farming was calculated using a stochastic frontier analysis model. The results are summarized in table 4.
Table 4. Results of estimation of technical efficiency of production function in tomato farming using the stochastic frontier approach.
| Variable | Coefficient | Standard Error | t-ratio |
|---|---|---|---|
| Production | 0.20927823 | 0.33438997 | 0.24201629 |
| Ln X1 (land) | 0.39187047 | 0.17841825 | 0.21963586 |
| Ln X2 (seeds) | 0.36392189 | 0.18391427 | 0.19787583 |
| Ln X3 (fertilizer) | -0.91620249 | 0.41882314 | -0.21875642 |
| Ln X4 (pesticide) | 0.28843663 | 0.29828452 | 0.96698491 |
| Ln X5 (labor) | 0.50747247 | 0.42540827 | 0.11929069 |
The mean technical efficiency for tomato farming was calculated as 0.759, indicating that farmers in the region are relatively efficient in their use of production factors. However, there is room for improvement. Table 4 reveals that land and seeds have a positive effect on production, while fertilizers and pesticides have a negative effect. This indicates that fertilizer use is not in accordance with the recommended use, either due to over-application or mismanagement. The closer the technical efficiency value is to 1, the more efficient the farming process. In this case, with a mean technical efficiency of 0.759, tomato farming can be considered technically efficient, though improvements in the management of certain inputs, especially fertilizer, could enhance productivity.
Price efficiency
Price efficiency measures how well the cost of inputs is aligned with their contribution to output. Table 5 presents the results of the price efficiency analysis for tomato farming in Tombolo Pao District.
Table 5. Results of price efficiency estimation in tomato farming using the stochastic frontier analysis.
| Input | NPMx | Px | NPMx/Px |
|---|---|---|---|
| Land | 83,416,667 | 5,005,000 | 1.666667 |
| Seeds | 69,458,333 | 416,750,000 | 0.166667 |
| Fertilizer | 20,126,667 | 120,760,000 | 0.166667 |
| Pesticides | 67,616,667 | 40,570,000 | 1.666667 |
| Labor | 12,666,667 | 76,000,000 | 0.166667 |
| Total | 506,570,002 | 659,085,000 | 3.833335 |
| Average | 253,570,001 | 131,817,000 | 0.766667 |
The overall price efficiency score of 3.83 indicates that tomato farming is not price-efficient. If the ratio of marginal product value (NPMx) to input price (Px) exceeds 1, the input is being used efficiently. In this case, land and pesticides are price-efficient, with ratios of 1.66 each, suggesting they are used effectively. Conversely, seeds, fertilizer, and labor show ratios of 0.16, indicating that these inputs are not being used optimally. To improve price efficiency, farmers should reduce the use of seeds, fertilizer, and labor or reassess their application methods to ensure a better return on investment.
Economic efficiency
Economic efficiency combines both technical and price efficiency to evaluate the overall efficiency of input use in the production process. The results of the economic efficiency analysis are presented in table 6.
Table 6. Results of economic efficiency estimation in tomato farming using the stochastic frontier analysis.
| Input | TE | PE | EE |
|---|---|---|---|
| Land | 0.75938806 | 1.66 | 1.26 |
| Seeds | 0.75938806 | 0.16 | 0.12 |
| Fertilizer | 0.75938806 | 0.16 | 0.12 |
| Pesticides | 0.75938806 | 1.67 | 1.27 |
| Labor | 0.75938806 | 0.16 | 0.12 |
| Total | 3.76969403 | 3.81 | 2.89 |
| Average | 0.75938806 | 0.76 | 0.57 |
An economic efficiency (EE) score greater than 1 indicates inefficiency, and in this case, the mean EE of 0.57 shows that tomato farming is not yet economically efficient. This inefficiency suggests that adjustments in input use are necessary to optimize production (Nakana et al., 2021). Land and pesticides demonstrate relatively higher economic efficiency (1.26 and 1.27, respectively), indicating their effective use. However, seeds, fertilizer, and labor show low economic efficiency (0.12 each), signaling significant inefficiencies in these areas.
CONCLUSION
The findings of this study indicate that land size and seed quality significantly influence tomato production, while fertilizer use showed a negative impact, possibly due to overuse or improper application. Pesticides and labor did not significantly affect production, highlighting the need for improved resource management in these areas. The technical efficiency of tomato farming was relatively high at 0.759, suggesting that while farmers are reasonably efficient, there is still potential for optimization.
The study contributes to the existing literature by providing empirical evidence on the efficiency of production factors in highland tomato farming, particularly in Indonesia. It underscores the importance of land and seed optimization while cautioning against excessive fertilizer use. This research offers practical implications for policymakers and farmers, encouraging the adoption of better seed varieties and more effective fertilization practices. Future research could explore the role of technology adoption and its impact on farming efficiency, as well as investigate other highland areas to generalize the findings. This study highlights the need for ongoing efforts to enhance resource management and input use in tomato farming.
