• V. Adah
  • P. O. Agada
Keywords: Climate, Climatic Condition, favourable, Crop, discrete time stochastic process, Markov Chain


Probabilistic Models that quantify and assess the short and long run impact of climate change on the growth of yam in Makurdi, Nigeria are rare; this is because most existing models only consider the distribution pattern of the climatic variables without crop requirements. This work has been able to develop a probabilistic modeling approach for this purpose. Data on daily  rainfall amount in the study area for the period of  34 years(1977-2010) were transformed into zeros (0) and ones (1) representing binary outcome of a random variable  defined as the climatic condition for the growth of yam in respect of rainfall.  The outcome   “1” represent   a favourable climatic condition while the outcome “0” represent an unfavourable climatic condition. The sequence of favourable and unfavourable outcomes formed   a discrete time stochastic process which was modeled   as aMarkov Chain. The n-step matrix of transition probabilities shows in probabilistic terms the results of the short run impact of climate change on the growth of yam while the converged or steady probability matrix shows the long run impact. For the seven – month phase, the study area is at risk of 79% negative impact of climate change on the growth of yam yearly and a favourability of 21% every four years.  It was recommended that farmers in Makurdi metropolis  should  invest less in yam production yearly and more every five years and that the modeling approach  be generalized for quantifying and assessing the impact of climate change on any crop and other climatic variables


Agada, P. O., Imande, M. T. and Ahmedu, M. O. (2018). Statistical Indicators of Climate change in Makurdi Metropolis: An Implication to crop production in the Area.The Journal of the Mathematical Association of Nigeria (Abacus), 45 (1): 198- 213.

Agada, P. O., Ogwuche, O. I. and Yawah, B.I. (2019). Probability Indicator Functions for Assessing the Impact of Climate Change on the Growth and Storage of Crops in Makurdi Metropolis: The Journal of the Mathematical Association of Nigeria (Abacus), 46 (1): 391- 406.

Aondoakaa, S. C. (2012). Effects of Climate Change in FCT, Abuja, Nigeria. Ethiopia Journal of Environmental Studies and Managerial, 4 (2)

Ayinde, O. E., Muchie, M. and Olatunji, G. B.(2011). Effect of Climate Change on Agricultural Productivity in Nigeria: A Co-integration Model Approach. Journal of human ecology.35 (3): 189-194.

Basak, K. (2009), Climate Change Impacts on Rice Production in Bangladesh: Results from a Model, Unnayan Onneshan-The Innovators, A Center for Research and Action on Development paper no. 243

Bhusal, M. K., (2018). Application of Markov Chain Model in the Stock Market Trend Analysis of Nepal, International Journal of Scientific and Engineering Research, 8 (10):1733-174

Enete, I.C. (2014). Impacts of Climate Change on Agricultural Production in Enugu State, Nigeria. Journal of Earth Science and Climatic Change, 5 (9)

Hillier, F.S. and Lieberman, G. J. (2010). Introduction to Operations Research (9th ed). Global Publisher: Raghothaman Srinivasan. Pp. 734-745.

Kar, S. K., Sahoo, D. P. and Subudhi, C. R.(2014). Weekly Rainfall Analysis for Crop Planning Using Markov’s Chain Model for Kandhamal District of Odisha, India. Journal of Engineering Research and Applications 4 (9):139-145.

Mijinyawa, Y and Akpenpuun, T. D. (2015). Climate change and its effect on grain crops yields in the middle belt in Nigeria. African Journal of Environmental Science and Technology. 9 (7):641-645.

Olajire, M. A. Matthew, O. J. . Omotara, O. A and Aderanti, A. (2018).Assessment of Food Crop Production in Relation to Climate Variation in Osun State Southwestern Nigeria. Journal of Agriculture and Ecology Research International 14 (2): 1-14

Raheem, M.A., Yahaya, W.B. and Obisesan. K.O. (2015). A Markov Chain Approach on Pattern of Rainfall Distribution. Journal of Environmental Statistics. 7 (1).

Sharma, J. K.(2010). Operation Reseacrh: Theory and Application (4th ed) . Macmillian. Pp. 660-663.

Uger, F. I.(2017). Impact of Climate Variability on Yam Production in Benue State: an Empirical Analysis. International Journal of Innovative Research in Social Sciences and Strategic Management Techniques, 4 (2) : 2467-8155

Yoo, C., Lee, J. and Ro, Y.,(2016).Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact. Advances in Meteorology

Yusuf, A. U., Adamu, L and Abdullahi, M. (2014). Markov chain model and its application to annual rainfall distribution for crop production American Journal of Theoretical and Applied Statistics, 3 (2): 39-43

Zakari, D. M., Mohammed, A.B., Medugu N.I. and Sandra I.( 2014). Impact of Climate Change on Yam Production in Abuja, International Journal of Science, Environment and Technology, 3 (2), 458 – 472

How to Cite
AdahV., & AgadaP. O. (2023). A PROBABILISTIC MODELLING APPROACH TO QUANTIFY AND ASSESS THE IMPACT OF CLIMATE CHANGE ON THE GROWTH OF YAM IN MAKURDI, NIGERIA. FUDMA JOURNAL OF SCIENCES, 3(4), 352 - 359. Retrieved from https://fjs.fudutsinma.edu.ng/index.php/fjs/article/view/1657