Paddy Production Analysis in Non-Granary Areas
Authors
Nur Aliah Najwa Ahmad Mahyudin
Faculty of Computer and Mathematical Sciences, University Technology MARA (UiTM) Cawangan Negeri Sembilan, Kampus Seremban, Negeri Sembilan (Malaysia)
Faculty of Computer and Mathematical Sciences, University Technology MARA (UiTM) Cawangan Negeri Sembilan, Kampus Seremban, Negeri Sembilan (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2025.910000139
Subject Category: Agriculture
Volume/Issue: 9/10 | Page No: 1640-1646
Publication Timeline
Submitted: 2025-09-27
Accepted: 2025-10-04
Published: 2025-11-06
Abstract
Currently, it has been shown that Malaysia has a low production of paddy. That is one of the reasons why Malaysia has been importing rice from other countries. To help Malaysia overcome the rice output shortage, this study has decided to determine whether there is an effect of planted area and paddy yield towards paddy production in a long-run scenario. This study focuses on analysing the paddy production of non-granary areas in Johor, Melaka, Negeri Sembilan, Pahang, Pulau Pinang, and Terengganu from 2011 until 2020. The paddy production that is being analysed is during the main season in Malaysia. In order to determine the presence of the long-run relationship, this study employed the Pedroni cointegration test. It was found that the majority of the statistical tests from the Pedroni cointegration test are significant at 5% level of significance. It can be concluded that there is a long-run relationship between planted area, paddy yield, and paddy production. Thus, it is recommended for future researchers to investigate in detail these effects to help maximise the paddy production, especially in the non-granary areas.
Keywords
cointegration test, paddy production, panel data, stationary tests
Downloads
References
1. Baltagi, B. H., & Kao, C. (2000). Nonstationary panels, cointegration in panels and dynamic panels: A survey. Advances in Econometrics [Google Scholar] [Crossref]
2. Barbieri, L. (2008). Panel Cointegration Tests: A Survey. Rivista Internazionale Di Scienze Sociali, 116(1), 3–36. https://www.jstor.org/stable/41625199 [Google Scholar] [Crossref]
3. Bashir, A., & Yuliana, S. (2019). Identifying factors influencing rice production and consumption in Indonesia. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 19(2) https://doi.org/10.23917/jep.v19i2.5939 [Google Scholar] [Crossref]
4. Bouchoucha, Yahyaoui, I., & Bouchoucha, N. (2019). The long-run relationship between ODA, growth and governance: An application of FMOLS and DOLS Approaches [Google Scholar] [Crossref]
5. Chandio, A. A., Jiang, Y., Ahmad, F., Adhikari, S., & Ain, Q. U. (2021). Assessing the impacts of climatic and technological factors on rice production: Empirical evidence from Nepal. Technology in Society, 66 101607. https://doi.org/10.1016/j.techsoc.2021.101607 [Google Scholar] [Crossref]
6. Department of Agriculture Peninsular Malaysia (2021). Paddy Production Survey Report Malaysia, Main Season 2020/2021. [Google Scholar] [Crossref]
7. Giulietti, M., Otero, J., & Smith, J. (2009). Testing for stationarity in heterogeneous panel data in the presence of cross-section dependence. Journal of Statistical Computation and Simulation, 79(2), 195–203. https://doi.org/10.1080/00949650701719136 [Google Scholar] [Crossref]
8. Örsal, D. D. K. (2007). Comparison of panel cointegration tests. Www.econstor.eu. http://hdl.handle.net/10419/25201 [Google Scholar] [Crossref]
9. Pariona, A. (2019). What are the World’s Most Important Staple Foods? WorldAtlas. https://www.worldatlas.com/articles/most-important-staple-foods-in-the-world.html [Google Scholar] [Crossref]
10. Statista. (2023a). Annual Rice Consumption in Malaysia 2019-2024. https://worldpopulationreview.com/country-rankings/rice-consumption-by-country [Google Scholar] [Crossref]
11. Statista. (2023b). Production of Rice in Malaysia 2013-2021. https://www.statista.com/statistics/794700/rice-production-volume-malaysia/ [Google Scholar] [Crossref]
12. Makhtar, S., Abidin, I. S. Z., & Islam, R. (2022). Reviewing food security on paddy production: A conceptual paper. International Journal of Industrial Management, 16(1), 51-58. [Google Scholar] [Crossref]
13. Win, E. P., Win, K. K., Bellingrath‐Kimura, S. D., & Oo, A. Z. (2020). Greenhouse gas emissions, grain yield and water productivity: a paddy rice field case study based in Myanmar. Greenhouse Gases: Science and Technology, 10(5), 884–897. https://doi.org/10.1002/ghg.2011 [Google Scholar] [Crossref]
14. World Population Review. (2024). Rice Consumption by Country 2023. Worldpopulationreview.com. https://worldpopulationreview.com/country-rankings/rice-consumption-by-country [Google Scholar] [Crossref]
15. Yohandoko, S., & Supriyanto, S. (2023). Panel Data Analysis on Rice (Paddy) Production in Indonesia 2018-2021. International Journal of Mathematics, Statistics, and Computing, 1(3), 44–53. https://doi.org/10.46336/ijmsc.v1i3.27 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Breeding for a Greener Future: Selective Breeding and Crossbreeding Approaches to Minimize Methane Emissions in Ruminant Livestock
- Determinants of Adoption of Post-Harvest Losses Prevention Techniques among Banana/Plantain Marketers in Lagos State, Nigeria
- Enhancing Rice Yield Prediction Using UAV-Based Multispectral Imaging and Machine Learning Algorithms
- Seed-Borne Fungi of Groundnuts (Arachis Hypogaea) and Their Management with Ginger (Zingiber Officinale) Extract In Makurdi, Nigeria
- The Influence of Landforms and Slope on Agricultural Cropping Patterns in Chhatrapati Sambhajinagar District