Issue |
E3S Web Conf.
Volume 232, 2021
International Conference on Agribusiness and Rural Development (IConARD 2020)
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Article Number | 01013 | |
Number of page(s) | 10 | |
Section | Agricultural Economic | |
DOI | https://doi.org/10.1051/e3sconf/202123201013 | |
Published online | 25 January 2021 |
Assessing Farmers’ Decision-Making in the Implementation of Jajar Legowo Planting System in Rice Farming Using a Logit Model Approach in Bantul Regency, Indonesia
Universitas Muhammadiyah Yogyakarta, Agribusiness Department, 55183 Yogyakarta, Indonesia
* Corresponding author: eniistiyanti@umy.ac.id
Jajar Legowo is one of the planting systems in rice farming. Proper implementation of this system can increase farmers’ production and income. This research aims to describe the characteristics of farmers based on internal and external factors and analyze the factors influencing of decision making in the implementation of the Jajar Legowo planting system. Respondents were taken using a stratified random sampling method, as many as 50 farmers consisting of 25 farmers who implemented the Jajar Legowo planting system and 25 farmers who used a conventional system. The characteristics of rice farmers were explained descriptively in a tabular form. The binary logit regression model was used to analyze the factors influencing farmers’ decision making. The results show most of the rice farmers implementing both the Jajar Legowo and the conventional systems were of productive age with elementary school education. The land area and income of Jajar Legowo farmers were higher than conventional farmers. The social and economic environments of both the planting system were already high. Nature of Innovation of the farmers applying Jajar Legowo planting system were high. The variables income, land area, and the nature of innovation influenced farmers’ decision making in implementing Jajar Legowo planting system.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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