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The effects of interyear variability of extreme rainfall events on maize yields at locations in Cameroon, in central‐west sub‐Saharan Africa were investigated through a simulation assessment combining a weather generator with a crop growth model. This study analyzes the potential of using dry/wet year predictions to reduce risk in subsistence agricultural production associated with climate variability at the site level. Weather data sets from eight provincial study localities were classified into three precipitation scenarios – dry (lower threshold), normal and wet (upper threshold) years. According to the modelling results, there is a less than 12 per cent variance in mean maize yields across six out of the eight localities when planting occurs in March, May and August. The variance is equivalent to approximately 100–300 kg per ha, which represents a significant amount of food in the household security of the majority impoverished sectors of rural and urban society, and which could greatly impact the socioeconomic activities of the entire populace. The results lead to the conclusion that all extreme dry and wet years are not equal in terms of their regional manifestation. This calls for precise monthly and sub‐seasonal local level forecasts and the effective dissemination of this information to farming communities in Cameroon, thereby facilitating the adaptive management of indigenous cropping practices and reducing their vulnerability to climate related disasters.
Tingem et al. (Sat,) studied this question.