Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

A.D.K AL-Hiyali

Department of Agricultural Economics, Agriculture College, University of Anbar, Ramadi, 31001, Iraq

Hayder Hameed Blaw

Agiculture College, Al-Muthanna University, Samawah, Al Muthanna Governorate, 66001, Iraq

Najlaa Salah Madlul

Agiculture College, University of Tikrit, Tikrit, Saladin Governorate, 34001, Iraq


Received: 18 December 2023; Received in revised form: 19 March 2024; Accepted: 20 March 2024; Published: 28 March 2024

Copyright © 2024 A.D.K AL-Hiyali, Hayder Hameed Blaw, Najlaa Salah Madlul. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

Keywords: Markov chains; Finite state machines; Predictive accuracy


[1] Andral, C., Douc, R., Marival, H., et al., 2024. The importance Markov chain. Stochastic Processes and their Applications. 171, 104316. DOI:

[2] Azizah, A., Welastika, R., Falah, A.N., et al., 2019. An Application of Markov Chain for Predicting Rainfall Data at West Java using Data Mining Approach. IOP Conference Series: Earth and Environmental Science. 303(1), 012026. DOI:

[3] Dynkin, E.B., 1960. Markov processes and related problems of analysis. Russian Mathematical Surveys. 15(2), 1–21. DOI:

[4] Krumbein, W.C., Dacey, M.F., 1969. Markov chains and embedded Markov chains in geology. Journal of the International Association for Mathematical Geology. 1, 79–96. DOI:

[5] Moscoso, Y.F., Rincón, L.F., Leiva-Maldonado, S.L., et al., 2022. Bridge deterioration models for different superstructure types using Markov chains and two-step cluster analysis. Structure and Infrastructure Engineering. 20(6), 791–801.‏ DOI:

[6] Cogburn, R., 1980. Markov chains in random environments: The case of Markovian environments. The Annals of Probability. 8(5), 908–916.‏ DOI:

[7] Caleyo, F., Velázquez, J.C., Valor, A., et al., 2009. Markov chain modelling of pitting corrosion in underground pipelines. Corrosion Science. 51(9), 2197–2207.‏ DOI:

[8] Stander, J., Farrington, D.P., Hill, G., et al., 1989. Markov chain analysis and specialization in criminal careers. The British Journal of Criminology. 29(4), 317–335. DOI:

[9] Godfrey, R., Muir, F., Rocca, F., 1980. Modeling seismic impedance with Markov chains. Geophysics. 45(9), 1351–1372.‏ DOI:

[10] Jain, S., 1986. Markov chain model and its application. Computers and Biomedical Research. 19(4), 374–378. DOI:

[11] Rezaeianzadeh, M., Stein, A., Cox, J.P., 2016. Drought forecasting using Markov chain model and artificial neural networks. Water Resources Management. 30, 2245–2259.‏ DOI:

[12] Saadi, I., Mustafa, A., Teller, J., et al., 2016. Forecasting travel behavior using Markov Chains-based approaches. Transportation Research Part C: Emerging Technologies. 69, 402–417.‏ DOI:

[13] Matis, J.H., Saito, T., Grant, W.E., et al., 1985. A Markov chain approach to crop yield forecasting. Agricultural Systems. 18(3), 171–187.‏ DOI:

[14] Khiatani, D., Ghose, U. (editors), 2017. Weather forecasting using hidden Markov model. 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN); 2017 Oct 12–14; Gurgaon, India. New York: IEEE.‏ p. 220–225. DOI:

[15] Zakaria, N.N., Othman, M., Sokkalingam, R., et al., 2019. Markov chain model development for forecasting air pollution index of Miri, Sarawak. Sustainability. 11(19), 5190.‏ DOI:

[16] Wilinski, A., 2019. Time series modeling and forecasting based on a Markov chain with changing transition matrices. Expert Systems with Applications. 133, 163–172.‏ DOI:

[17] Yapo, P., Sorooshian, S., Gupta, V., 1993. A Markov chain flow model for flood forecasting. Water Resources Research. 29(7), 2427–2436. DOI:

[18] Alevizos, E., Artikis, A., Paliouras, G. (editors), 2017. Event forecasting with pattern markov chains. Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems; 2017 Jun 19–23; Barcelona, Spain. p. 146–157.‏ DOI:

[19] Alani, L.A.F., Alhiyali, A.D.K., 2021. Forecasting wheat productivity in Iraq for the period 2019–2025 using markov chains. Iraqi Journal of Agricultural Sciences. 52(2), 411–421. DOI:

[20] Bosabt, A.K., 2015. Using Markov chains to predict wheat productivity in Algeria. Journal of Human Sciences. 26(1).171-183 (in Arabic)

[21] Jain, R.C., Agrawal, R., 1992. Probability model for crop yield forecasting. Biometrical Journal. 34(4), 501–511. DOI:

[22] The Use of Markov Optimization Models in the Economic and Ecological Management of Forest Landscapes under Risk [Internet]. Available from:

[23] Kachapova, F., 2013. Representing Markov chains with transition diagrams. Journal of Mathematics and Statistics. 9(3), 149–154. DOI:

[24] Jain, V., Lande, B. K., 2012. Computation of the State Transition Matrix for General Linear Time-Varying Systems, International Journal of Engineering Research And Technology. 1(6). 2012

[25] Vidal, E., Thollard, F., De La Higuera, C., et al., 2005. Probabilistic finite-state machines-part I. IEEE transactions on pattern analysis and machine intelligence. 27(7), 1013-1025. DOI:

[26] Brand, D., Zafiropulo, P., 1983. On communicating finite-state machines. Journal of the ACM (JACM). 30(2), 323-342. DOI:

[27] Voskoglou, M.G., 2016. Applications of finite Markov chain models to management. arXiv preprint arXiv:1601.01304. DOI:

Online ISSN: 2737-4785, Print ISSN: 2737-4777, Published by Nan Yang Academy of Sciences Pte. Ltd.