Chemical stratification occurs under no-till systems, including pH, considering that higher levels are formed from the soil surface towards the deeper layers. The subsoil acidity is a limiting factor of the yield. Gypsum has been suggested when subsoil acidity limits the crops root growth, i.e., when the calcium (Ca) level is low and/or the aluminum (Al) level is toxic in the subsoil layers. However, there are doubts about the more efficient methods to estimate the gypsum requirement. This study was carried out to develop numerical models to estimate the gypsum requirement in soils under no-till system by the use of Machine Learning techniques. Computational analyses of the dataset were made applying the M5’Rules algorithm, based on regression models. The dataset comprised of soil chemical properties collected from experiments under no-till that received gypsum rates on the soil surface, throughout eight years after the application, in Southern Brazil. The results showed that the numerical models generated by rule induction M5’Rules algorithm were positively useful contributing for estimate the gypsum requirements under no-till. The models showed that Ca saturation in the effective cation exchange capacity (ECEC) was a more important attribute than Al saturation to estimate gypsum requirement in no-till soils.
