Abstract
African agriculture is adversely impacted by arable soil compaction, the degree of which is affected by the speed at which the tractor is maneuvered on the fields, which affects the degree of soil compaction. However, there is no reliable, existing mathematical correlation between the extent of compaction on the one hand, and the tractor speed/s and soil moisture levels on the other. This paper bridges this gap in knowledge by resorting to the artificial neural networks (ANNs) method to predict the effects of tractor speed and soil moisture on the state of soil compaction. The models were ‘trained’ with penetration resistance (CPR) and bulk density test data obtained from field measurements. The resulting correlation coefficient (R = 0.9) showed good compliance of the prediction made with the ANN models with on-field data. It follows, thereby, that the model developed by the authors in this study can be effectively used for predicting the effects of speed, soil density, and moisture content on compaction of alluvial, poorly developed soil with much greater precision, thereby providing guidance to farmers around the world.