DOI : https://doi.org/10.18517/ijaseit.14.4.17411

Input-Output Modeling in Rice Farming: An Empirical Evidence from Indonesia

Aylee Christine Alamsyah Sheyoputri (1), Faidah Azuz (2)
(1) Department of Agribusiness, Faculty of Agriculture, Bosowa University, Makassar, Indonesia
(2) Department of Agribusiness, Faculty of Agriculture, Bosowa University, Makassar, Indonesia
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How to cite (IJASEIT) :
Sheyoputri, Aylee Christine Alamsyah, and Faidah Azuz. “Input-Output Modeling in Rice Farming: An Empirical Evidence from Indonesia”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1291-7, doi:10.18517/ijaseit.14.4.17411.
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Food sovereignty is a social, political, and economic issue. One of the key food sources is rice, which is developed through rice farming. This makes rice farming a strategic role in people's lives and becomes a bargaining instrument for the State. Therefore, building a rice farming input-output model is important for farmers as farming actors, the public, and the government as a policy maker. This study aims to build a rice farming input model and, secondly, to analyze the role of each input on rice production in Indonesia. The practical implications of this research are significant, as it provides a comprehensive understanding of the factors influencing rice production, which can inform policy decisions and improve farming practices. This study was conducted in Turikale Subdistrict, Maros Regency, South Sulawesi. Data was obtained from 247 farmers. The analysis was conducted using multiple regression analysis in which the dependent variable is rice production, while the independent variables are land, seeds, fertilizers, pesticides, labor, and farm management. The results showed the input-output model Y = 222.126 + 2834.494X1 + 65.164X2 + 0.541X3-0.630X4 + 0.123X5 + 758.749X6 + ε. From the F test, it is known that all independent variables together (simultaneously) affect the dependent variable at the 95% confidence level. While the t-test is known to be partially variable, land, seeds, and crop management significantly affect rice production. Three other variables, fertilizers, pesticides, and labor do not significantly affect rice farming production. This research combines two aspects in one input-output analysis: technical aspects (land, seeds, fertilizers, pesticides, and labor) and non-technical aspects, namely crop management. Combining two aspects in one input-output modeling is statistically possible and, at the same time, finds its novel starting point.

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