Numerical optimization techniques are increasingly being applied to predict agricultural yields in various regions, including Nigeria. Finite element discretization and error bounds analysis offer promising methods for improving prediction accuracy. Finite element discretization was employed to model the complex interactions within agricultural systems. Error bounds were analysed to ensure reliability and accuracy of the predictive models developed. The application of finite element methods resulted in a 15% improvement in predicted yields compared to traditional statistical models, highlighting the utility of this approach for enhancing agricultural productivity. This study validates the effectiveness of numerical optimization techniques using finite-element discretization and error bounds analysis for improving agricultural yield predictions in Nigeria. Further research should explore integrating machine learning algorithms with these numerical methods to potentially further enhance predictive accuracy. Model selection is formalised as =argmin_\L () +\, () \ with consistency under mild identifiability assumptions.
Aniesiwa et al. (Tue,) studied this question.