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The study aimed at investigating the factors affecting the Cameroonian agricultural export such as GDP, population, cereal production, and arable land. Time series data from 1980 to 2021 was used. The data was collected from the Food and Agricultural Organization and the World Development Indicators. The autoregressive distribution lag model (ARDL) was used to test the relationship between the given variables. Results show that variables were stationary at the level of the first difference. The bound testing results also indicated that there is co-integration between the agricultural exports and other possible variables. The results from the ARDL model indicated that an increase in cereal production of 1% will lead to an increase in agricultural exports of 1.83% in the long run. Surprisingly, arable land reduces agricultural exports by 11.49% in the long run. In the short run, population and arable land enhance agrarian exports. The Granger causality test estimation, on its part, proved the evidence of unidirectional causality running from GDP and population towards agricultural exports. Policymakers should implement policies to increase cereal production and optimize the use of arable land to boost Cameroonian agricultural exports.
Samka et al. (Sat,) studied this question.