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, 2023)
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
Keywords: Climate, Climatic Condition, favourable, Crop, discrete time stochastic process, Markov Chain