MARKOV CHAIN MONTE CARLO MODELLING OF THE IMPACT OF CLIMATE CHANGE ON THE GROWTH OF MILLET AND SOYBEAN IN NORTH CENTRAL, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2026-1008-5394Keywords:
Joint Binary Markov Process, Rainfall–Temperature Dynamics, MCMC Estimation, Gelman–Rubin Diagnostic, Climate Variability, Soybean Production, Millet ProductionAbstract
This study investigates the spatio-temporal dynamics of rainfall and temperature suitability for soybean and millet production across states in North Central Nigeria using a joint binary Markov model. Monthly rainfall and temperature data obtained from the National Aeronautics and Space Administration (NASA) for the period 1984–2022 were classified into favourable (1) and unfavourable (0) crop-growth conditions based on crop-specific requirements. Four joint climatic states, (0,0), (0,1), (1,0), and (1,1), were defined to represent combined rainfall-temperature suitability. Transition probability matrices were estimated to assess climatic persistence and state transitions, while MCMC methods were used for parameter estimation. Convergence and reliability of the posterior estimates were confirmed using the Gelman–Rubin diagnostic.The results revealed marked spatial differences in climatic suitability across the study area. Benue and Kwara exhibited the highest persistence of favourable climatic conditions for millet production, with probabilities of remaining in the favourable state (1,1) of 0.6843 and 0.6661, respectively, while Kogi also showed strong climatic stability (0.5675). In contrast, Plateau displayed the highest persistence of unfavourable conditions, remaining in state (0,0) with probability 0.6668. Niger and the FCT showed greater climatic variability, whereas Nasarawa exhibited mixed persistence in both favourable (0.5483) and unfavourable (0.5345) states. These findings highlight significant regional variations in rainfall–temperature suitability and provide useful information for agricultural planning, climate risk management, and adaptation strategies. The joint binary Markov model estimated using MCMC offers a robust framework for assessing climate variability and its implications for soybean and millet production.
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Copyright (c) 2026 Victoria Adah, Jonathan Atsua Ikughur, Depuun Nicholas Temaugee
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