Mathematical modeling for management of stored-grain ecosystems: Approaches, opportunities, and research needs

Abstract

Cereal grains, oilseeds, and pulses (collectively referred to as grains) form a major portion of daily intake for humans and domesticated animals and hold greater economic value for producers and grain industry. Often grain losses (qualitative and quantitative) occur due to improper management of grains during storage. Grain storage comprises of several interactions among biotic and abiotic factors that makes understanding of ecosystem quite complex. Over the years, mathematical modeling has emerged as a powerful tool for assessment, prediction, and simulation of real-time storage conditions. This manuscript presents a comprehensive review on various modeling approaches that are used for solving grain storage problems. Different solution techniques of mathematical formulations using analytical and numerical approaches (finite element, finite difference, finite volume, and discrete element modeling) are explained. The testing and validation are critical steps and must be considered during model development process. Reports are available for the prediction of temperature, moisture, and gas diffusion profiles in grain bins for different storage conditions. Similarly, works have been attempted for determination of spatial temporal distribution of stored products insect in grain bulks as well as models to predict development of fungi in grains have been reported. However, a comprehensive grain storage model through coupling of physical models (thermal, moisture, and gas diffusion) with biological models (population dynamics and dispersal) as well as economic models is needed. The advancements in information technology would aid in analyzing the available data from laboratory and field studies for forming an online database. Appropriate global cooperation and coordination of available data could help in accessing information about stored grains and level of infestation or infection at any given time. Early prediction of storage conditions in grain bins are possible through mathematical modeling approach that should help in establishing better grain management protocols.