Entropy-Based Information Theory Framework for Uncertainty Quantification in Agricultural Engineering Systems in Nigeria

This study develops an entropy-based information theory framework for systematic uncertainty quantification in agricultural engineering systems in Nigeria, providing a theoretically rigorous methodology for decision-making under the multiple sources of uncertainty that characterise Nigerian agricultural engineering contexts. Agricultural engineering design and management in Nigeria is subject to deep uncertainty arising from variable material properties, imprecise loading specifications, unpredictable climatic conditions, uncertain model parameter values, and limited monitoring data. Current engineering practice manages this uncertainty through ad hoc safety factors that may be insufficiently calibrated to actual uncertainty levels. Information-theoretic approaches based on Shannon entropy and related concepts offer a principled framework for characterising, propagating, and reducing uncertainty in complex engineering systems. This study applies maximum entropy principles, Kullback-Leibler divergence analysis, and Bayesian updating within a unified uncertainty quantification framework applied to three engineering system classes: irrigation hydraulic structures, grain storage facilities, and solar-powered agro-processing systems. Case studies in each system class use field monitoring data from sites in Kebbi, Kano, and Enugu States to demonstrate the framework application. Entropy-based sensitivity analysis identifies the information sources whose uncertainty most strongly influences system performance, guiding targeted data collection priorities. Findings reveal that conventional Nigerian engineering safety factors are both over- and under-conservative depending on the uncertainty source, with hydraulic structures showing under-conservative designs for uncertain flood loading while structural foundations show over-conservative designs. The study contributes an original entropy-based uncertainty framework for Nigerian agricultural engineering and recommends its integration into professional engineering practice standards.

Keywords: entropy, information theory, uncertainty quantification, agricultural engineering, Nigeria.