Climate variability and change is expected to affect agricultural productivity among other sectors. Studying the influence of this variability on crop production is one measure of generating climate change resilience strategies. In this study, the influence of climate variability on crop yield is investigated by determining the degree of association between climatic indices and crop yields of maize and sorghum using spearman’s rank correlation. The climatic indices used in this study are aridity index (AI), standardised precipitation evapotranspiration index (SPEI) at timescales of 1, 3, 6 and 12 months and southern oscillation index (SOI) representing El Nino southern oscillation (ENSO) influence on local climate. Local rainfall characteristics are expressed through length of the rainy season (LRS). Results reveal that ENSO influence is the most dominant across Botswana accounting for 85% and 78% variations in maize and sorghum yields respectively. Whereas AI and SPEI accounts for 70% and 65% variations in maize and sorghum respectively, LRS accounts for only 50% and 62% respectively. To facilitate agricultural planning, crop yield projections have been made using artificial neural network (ANN) models. The ANN projections indicate a likelihood of maize and sorghum yields declining by 51% and 70% respectively in the next 5 years. The high association between ENSO and crop yields in Botswana could further facilitate yield projections. Information generated from this study is useful in agricultural planning and hence strengthens farmers’ strategies in mitigating impacts of climate variability and change in semiarid areas.

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