THE DYNAMICS OF AGRICULTURAL OUTPUT VALUE IN TANZANIA: AN AUGMENTED DICKEY-FULLER ANALYSIS
Keywords:
Stationary, Agricultural output, Augmented Dickey-fuller testAbstract
Despite its existing bottlenecks, Agriculture remains a key driving sector for economic transformation of Tanzania and Africa continent largely. This study analyzes the dynamics of agricultural output value in Tanzania. The novelty of this study is the application of Structural explanation approach; Dickey-Fuller (DF) test and its standard version of Augmented DickeyFuller (ADF) Test Model to analyze data on agricultural output value from 1990-2021 for assessing the stationarity and mean-reverting behavior of the sector. The findings indicated that, after differencing, the agricultural output series exhibits stationary characteristics, allowing for reliable forecasting. The results further revealed a significant negative relationship between past and current output levels, suggesting that increases in agricultural production are often followed by declines, reflecting the need for sustainable management practices. Furthermore, the model's diagnostics confirm its robustness, enabling accurate predictions of future output. These insights have important policy implications, emphasizing the necessity for strategies that enhance resilience and sustainability in agriculture. By investing in adaptive practices and technologies, policymakers can better equip farmers to manage production variability and mitigate the impacts of external shocks. This study contributes to the understanding of agricultural output dynamics and provides a framework for effective decision-making aimed at improving food security and economic stability in the sector. Overall, the application of ADF modeling presents a baseline tool for forecasting agricultural output and guiding policy interventions that promote sustainable growth.
References
1. Baijukya, F. P., Sabula, L., Mruma, S., Mzee, F., Mtoka, E., Masigo, J., ... & Swai, E. (2020). Maize production manual for smallholder farmers in Tanzania.
2. Berman, P. S., Jones, J., Udry, J. R., & National Longitudinal Study of Adolescent Health. (2020). Research design. The SAGE Handbook of Political Science, 437.
3. Cai, Y., & Omay, T. (2021). Using double frequency in Fourier Dickey–Fuller unit root test. Computational Economics, 1-26.
4. Changalima, I. A., & Ismail, I. J. (2022). Agriculture supply chain challenges and smallholder maize farmers’ market participation decisions in Tanzania. Tanzania journal of agricultural sciences, 21(1), 104-120.
5. Enders, W. (2014). Applied Econometric Time Series (4th ed.). Wiley
6. Fouedjio, F. (2021). Stationarity. In Encyclopedia of Mathematical Geosciences (pp. 1-5). Cham: Springer International Publishing.
7. Gupta, V. (2020). A case study on economic development of Tanzania. Journal of the International Academy for Case Studies, 26(1), 1-16.
8. Gwambene, B., Liwenga, E., & Mung’ong’o, C. (2023). Climate change and variability impacts on agricultural production and food security for the smallholder farmers in Rungwe, Tanzania. Environmental Management, 71(1), 3-14.
9. Ilembo, B., & Kassim, R. (2025). The Power of first differencing in addressing non stationarity time series: Empirical evidence from price of rice using SARIMA model, Tanzania. Eastern Africa Journal of Contemporary Research, 5(1), 53-69.
10. Islam, M., Akhtar, A., Munira, S., Khan, M. S., & Murshed, M. M. (2018). Optimized Dickey-Fuller test refines sign and boundary problems compare to traditional Dickey-Fuller test. International Journal of Statistics and Probability, 7(5), 19-27.
11. Kabini, D. M. (2022). The impact of the agricultural sector on economic growth and development in South Africa (Doctoral dissertation, North-West University (South Africa)).
12. Kyojo, E. A., Mirau, S., Osima, S. E., & Masanja, V. G. (2024). Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania. Scientific African, 25, e02321.
13. Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in neural information processing systems, 35, 9881-9893.
14. Mhagama, G., Kimaro, M., & Otieno, S. (2023). Contributions of Agricultural Sub-sectors to Economic Growth in Tanzania Mainland: 2010-2018. African Journal of Accounting and Social Sciences, 5(1).
15. Mpogole, H., Dimoso, P., & Mayaya, H. (2020). Agriculture for Rural Development in Tanzania. TEMA Publishers Co. Limited, Dar es Salaam.
16. MUSSA, M. A. (2020). Exploring factors affecting agricultural productivity in Tanzania (Doctoral dissertation, KDI School).
17. National Bureau of Statistics (NBS) (2021). National Sample Census of Agriculture 2019/20. United Republic of Tanzania. https://www.nbs.go.tz/uploads/statistics/documents/en-1705482872-2019-20_Agri_Census_Atlas.pdf
18. Ndlovu, Z. S. (2023). Agricultural Shocks on household stability: A Case study of Tanzania Maize Farmers.
19. Oguwuike, W. T. (2018). Effect of agricultural output on economic growth in Nigeria (1981-2016). International Journal of Advanced Studies in Ecology, Development and Sustainability, 5(1), 39-53.
20. Otero, J., & Baum, C. F. (2018). Unit-root tests based on forward and reverse Dickey–Fuller regressions. The Stata Journal, 18(1), 22-28.
21. Paparoditis, E., & Politis, D. N. (2018). The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econometric Reviews, 37(9), 955-973.
22. Paul, F., & Lema, A. (2018). The dynamic synergies between agricultural financing and economic growth of Tanzania. African Journal of Economic Review, 6(2), 46-60.
23. Ramakgasha, M. J., Thaba, T. K., & Rudzani, N. (2024). Agricultural production and agricultural employment rate in South Africa: Time series analysis approach. International Journal of Economics and Financial Issues, 14(4), 148-153.
24. Roza, A., Violita, E. S., & Aktivani, S. (2022). Study of inflation using stationary test with augmented dickey fuller & phillips-peron unit root test (Case in bukittinggi city inflation for 2014-2019). EKSAKTA: Berkala Ilmiah Bidang MIPA, 23(02), 106-116.
25. Salles, R., Belloze, K., Porto, F., Gonzalez, P. H., & Ogasawara, E. (2019). Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164, 274-291.
26. Saxena, K. K., & Mhohelo, D. R. (2020). Modelling and forecasting retail prices of maize for three agricultural markets in Tanzania.
27. Ud Din, M. A., & Haseen, S. (2024). Impact of climate change on Indian agriculture: new evidence from the autoregressive distributed lag approach. Asia-Pacific Journal of Regional Science, 8(2), 377-394.
28. Wang, J., Ji, T., & Li, M. (2021, September). A combined short-term forecast model of wind power based on empirical mode decomposition and augmented dickey-fuller test. In Journal of Physics: Conference Series (Vol. 2022, No. 1, p. 012017). IOP Publishing.
29. Wineman, A., Jayne, T. S., Isinika Modamba, E., & Kray, H. (2020). The changing face of agriculture in Tanzania: Indicators of transformation. Development Policy Review, 38(6), 685-709.
