RJOAS January 2025
by
Kuntariningsih Endah Subekti, Department of Agribusiness, University of Diponegoro, Semarang, Indonesia
Whep Bambang, Department of Animal Science, University of Diponegoro, Semarang, Indonesia
Setiadi Agus, Department of Agribusiness, University of Diponegoro, Semarang Indonesia
Although traditionally sidelined in favor of rice, corn is recognized as a crucial local commodity for food security. In 2022, corn production from Gunungkidul farmers decreased by 1.20% to around 228,861 tons, while in 2023 it reached 276,589 tons, an increase of 1.20% from 2022. This study aims to identify the factors influencing corn farming production in three sub-districts benefiting from the Upsus (Special Efforts) program in Gunungkidul Regency. Primary and secondary data were used to answer the research objectives. A sample of 213 farmers was taken randomly by applying proportionate random sampling that based on three sub-districts: Saptosari, Tanjungsari, and Semin. The primary data were taken using interviews and questionnaires. Data were analyzed using multiple linear regression to determine the factors that mostly influence corn productivity. Based on the regression results, several factors significantly influence productivity. Seed usage and fertilizer usage have positive and statistically significant effects on productivity, with coefficients of .024 and .091, respectively, indicating that increasing these inputs improves productivity. Conversely, farm size, farming experience, and the number of labors negatively impact productivity, with coefficients of -.947, -.206, and -.024, respectively, and all were statistically significant (p-values < 0.05). These findings suggest that while optimizing seed and fertilizer usage can enhance productivity, larger farm sizes, more experience, and higher labor use may be associated with inefficiencies, resource mismanagement, or diminishing returns in this agricultural context.
Corn (Zea mays L.) is a globally significant crop with high economic value, serving as a primary source of food, livestock feed and industrial raw materials (Ainun et al., 2023). Originating from Mexico around 10,000 years ago, corn has spread worldwide, including to Indonesia. In Indonesia, corn is a vital agricultural commodity grown in various regions, including Gunungkidul Regency, Yogyakarta. This area is characterized by its fertile and diverse agricultural land, making corn a key crop alongside rice. With fertile soil and continued support programs, Gunungkidul Regency has the potential to become a competitive center for corn farming in Indonesia.
Corn production in Gunungkidul not only fulfills local consumption needs but also supports the livestock feed industry and serves as an industrial raw material, significantly contributing to farmers’ income (Pramono et al., 2020). However, corn agribusiness in the region faces several challenges, such as pest infestations (e.g., stem borers) and diseases like downy mildew, which require effective management and proper pesticide use. Additionally, limited access to advanced farming technology, high-quality seeds, fertilizers, and technical extension services hinders the adoption of efficient farming practices. Unpredictable climate conditions, including irregular rainfall, further threaten the productivity and sustainability of corn farming in this region (Al-Baarri, 2020).
Data from the Central Bureau of Statistics of Gunungkidul Regency show that corn production reached 275,913 tons in 2021, a 31.7% increase compared to the target of 209,501 tons of dry shelled corn in 2020. However, in 2022, production decreased by 1.20% to 228,861 tons, before increasing again to 276,589 tons in 2023, marking a 1.20% rise from the previous year (Central Statistical Bureau of Gunungkidul Regency, 2023). Given these fluctuating trends, the Gunungkidul Government needs to strengthen sustainable and innovative agricultural strategies. Recognizing their critical role, the local government, along with relevant institutions, has implemented the Special Efforts (UPSUS) program initiated by the Ministry of Agriculture. This program aims to enhance corn production and productivity, positioning it as a strategic local commodity while increasing farmers’ incomes. Initiatives under the program include the introduction of modern farming tools, the distribution of high-quality seeds, and improved access to fertilizers.
Independent variables such as seed usage, fertilizers, labor, land area, production costs, and experience in farming significantly affect agricultural productivity because they directly determine the quantity and quality of inputs used in the production process. Research utilizing the Cobb-Douglas production function model indicates that increasing inputs such as land area, the amount of fertilizer, or labor simultaneously tends to boost production output until reaching a certain scale of returns. For instance, in Central Java, these variables explain 92.4% of the variation in rice production, with significant contributions from each factor at a confidence level of up to 99% (Khakim et al., 2013). These variables also demonstrate positive production elasticity, indicating that optimizing inputs can efficiently enhance yields (Sentosa and Hidayat, 2021). This study analyzed the determinants of agricultural productivity, particularly in corn farming, which is crucial because corn is a strategic commodity that plays a vital role in food security and farmers’ economic stability. Understanding the relationship and contribution of each factor helps improve farming efficiency and supports evidence-based policy making to enhance yields and farmers’ income. The multiple linear regression method was employed as it can measure the simultaneous relationship between several independent variables (inputs) and the dependent variable (productivity). This regression approach allows for identifying which variables significantly influence productivity and their respective contributions.
The study applied a quantitative approach. The interview method with questionnaires and observation was used for collecting data. The locations in this study were determined purposively based on the criteria of sub-districts that benefited from the Special Program for Corn and had been running for 1 year, resulting in 3 sub-districts: Saptosari Sub-district, Tanjungsari Sub-district, and Semin Sub-district. The research sample consisted of farmers who are beneficiaries of the special corn program in three sub-districts (Saptosari, Tanjungsari, and Semin), with a total population of 8,986 individuals. A sample of 213 respondents was obtained from the total population using the Slovin method, and the distribution of samples across the three sub-districts was determined using proportionate random sampling. The data obtained were presented in the form of statistics, including tables, diagrams, and measurable figures, and the results are then explained descriptively in a quantitative manner. The data were analyzed quantitatively using multiple linear regression analysis to identify which variables significantly influence productivity and their respective contributions.
The results of the classical assumption tests in Table 1 indicate that the regression model satisfies all key assumptions. The Kolmogorov-Smirnov test shows a p-value of .200, which is greater than the significance level of .05, confirming that the residuals follow a normal distribution. For multicollinearity, all tolerance values are above .1, and VIF values are below 10, indicating no significant multicollinearity among the independent variables. This ensures the variables are sufficiently independent for reliable regression estimates.
The heteroscedasticity test results reveal that the p-values for all variables exceed .05, showing no significant heteroscedasticity. Although the p-value for fertilizer usage is close to the threshold at .053, it still indicates homoscedasticity (constant variance of residuals). Overall, the regression model satisfies the assumptions of normality, no multicollinearity, and homoscedasticity, making it statistically valid and robust for further analysis.
The regression results (Table 1) show that the constant of 15.920 (p < 0.05) indicates the value of the dependent variable when all independent variables are zero.
Seed usage (X₁) with a regression coefficient of .024 (p = .000) indicates a significant positive relationship; for every 1% increase in seed usage, productivity will increase by .024%, assuming other variables remain constant. Some studies have proved that the use of high-quality or certified seeds has a significant positive impact on agricultural productivity. Anne et al. (2022) argued that improved seed varieties, such as genetically modified or certified seeds, are designed to enhance crop yields by incorporating traits like drought tolerance, pest resistance, and faster germination. For example, genetically modified corn seeds have enabled farmers to plant more densely, expand cultivation to previously unsuitable areas, and reduce losses caused by pests and water shortages. These advancements have substantially increased productivity and made farming more efficient. Moreover, Okello et al. (2017) believed that the adaptability of advanced seed varieties to local climatic conditions makes farming more sustainable. For instance, drought-resistant seeds have reduced reliance on irrigation in water-scarce regions, improving the sustainability of farming practices. In areas with limited access to water resources, these seeds help farmers maintain productivity even under challenging environmental conditions.
Fertilizer usage (X₂) have a regression coefficient of 0.091 (p < 0.007), indicating a highly significant positive relationship; every increase in fertilizer usage is associated with a 0.091% increase in productivity. The use of fertilizers significantly impacts agricultural productivity, as they provide essential nutrients that plants require for growth. Studies show that without fertilizers, crop yields can decrease substantially. For example, corn yields in the U.S. would drop by 40% without nitrogen fertilizers, and similar reductions are observed in other crops like wheat and rice when key nutrients such as nitrogen, phosphorus, and potassium are insufficient (Hellums, 2020). Mikkelsen (2023) stated that in sub-Saharan Africa, where fertilizer usage is below recommended levels, low application rates contribute to soil degradation and reduced productivity. Increasing fertilizer application to optimal levels can enhance soil fertility, boost crop yields, and even mitigate land conversion for agriculture by intensifying production on existing farmland.
The variable farm size (X₃), with a regression coefficient of -.947 (p = 0.000), shows a significant negative relationship. This indicates that for every one-unit increase in farm size, productivity decreases by .947 units, assuming other variables remain constant. The negative sign signifies an inverse relationship between farm size and productivity. This contradicts the study by Wardani et al. (2019), which demonstrated the significant influence of land size as an independent variable on the income of corn farmers in East Lampung Regency. Their findings highlighted that larger land holdings lead to increased production. Furthermore, the system dynamics model proposed by Suryani et al. (2022) also emphasized the critical role of land size in improving production and income levels among corn farmers. Such contradictions occur because of the majority of land in Gunungkidul Regency consist of dry-lands, and formed by karst rock. Therefore, an increase in land size does not necessarily provide an impact on agricultural productivity.
The variable farming experience (X4), with a regression coefficient of -.206 (p = 0.000), shows a significant negative relationship. This indicates that for every one-unit increase in farming experience, productivity decreases by .206 units, assuming other variables remain constant. This contradicts some studies that proved that farming experience has a notable impact on agricultural productivity, as it influences farmers’ decision-making, resource management, and ability to adopt improved practices. Zhou and Li (2022) stated that experienced farmers tend to utilize inputs more efficiently, have better knowledge of soil and crop management, and are more likely to adopt advanced technologies, all of which contribute to higher productivity. Sugiantara and Utama (2019) also argued that farming experience can influence farmers’ productivity. As farmers gain more experience over time, their productivity tends to increase indirectly. However, traditional farmers often relies heavily on manual work, which is time-consuming and less efficient compared to mechanized farming. In addition, in many regions, the agricultural workforce is aging, with fewer younger workers entering the field. This demographic shift reduces the availability of active labor and slows the transition to more modern practice, making farming experience as a non promising factor in increasing productivity (Qing et al., 2019).
The variable labors (X5), with a regression coefficient of -.024 (p = 0.000), shows a significant negative relationship. This indicates that for every one-unit increase in labors, productivity decreases by .024 units, assuming other variables remain constant. Wang et al. (2022) stated that labor plays a significant role in farm productivity, influencing both output quantity and quality. Over time, U.S. agricultural productivity has increased even as labor input has declined, indicating that technological advancements and more efficient use of remaining labor have compensated for reduced workforce numbers. However, Qing et al. (2019) mentioned that traditional laborers may lack the training needed to adopt and utilize modern agricultural technologies or practices effectively. This can result in suboptimal use of resources, lower yields, and resistance to innovation. Investment in human capital—such as education and training—has been a key factor in driving productivity improvements, particularly as the farm sector has become more specialized and mechanized. Therefore, an increase in the number of agricultural laborers does not necessarily lead to higher productivity, as there is a need for laborers to adapt to technological advancements and undergo further training and education to improve their quality as agricultural workers.
Several factors play a significant role in influencing productivity. Seed usage and fertilizer usage positively impact productivity, with coefficients of .024 and .091, respectively, suggesting that increasing these inputs leads to improved productivity. In contrast, farm size, farming experience, and labor usage have negative effects on productivity, with coefficients of -.947, -0.206, and -.024, respectively, all statistically significant (p-values < 0.05). These results indicated that while increasing seed and fertilizer inputs can boost productivity, larger farm sizes, greater experience, and higher labor inputs may contribute to inefficiencies, poor resource allocation, or diminishing returns in agricultural practices.
Based on the results of the study, optimizing seed and fertilizer usage is crucial to improving productivity. Farmers should be encouraged to use high-quality seeds and apply fertilizers in appropriate quantities. Providing training on best practices for seed and fertilizer use, along with access to affordable inputs, can help enhance efficiency. Additionally, the negative relationship between farm size and productivity highlights potential management challenges on larger farms. Efforts should focus on promoting resource allocation strategies, modern farming techniques, and mechanization to mitigate inefficiencies in large-scale farming operations. The findings also suggest a need to reevaluate labor deployment and farming experience in productivity. The negative impact of labor usage may indicate overemployment or inefficiencies, which can be addressed by promoting labor-saving technologies and efficient task management. Furthermore, while experienced farmers may rely on traditional methods, targeted training programs that introduce modern agricultural techniques can help them improve productivity. Supporting small to medium-scale farmers through access to credit, markets, and resources is also vital to maintaining efficiency without the need for excessive farm expansion.
Original paper, i.e. Figures, Tables, References, and Authors' Contacts available at http://rjoas.com/issue-2025-01/article_13.pdf