Mathematical Models for the Prevention and Management of Cereal Crop Diseases: A Systematic Review

Mathematical modeling plays an important role in understanding and managing diseases that threaten cereal crop production worldwide. This systematic review, conducted in accordance with PRISMA guidelines, examines mathematical models developed for the prevention and management of diseases affecting maize, rice, wheat, barley, and sorghum. Relevant studies published from 2000 to 2026 were identified through systematic searches of major scientific databases, including Web of Science, Google Scholar, and PubMed. Of the 258 records retrieved, 26 studies met the eligibility criteria and were included in the review. The findings show that deterministic compartmental ordinary differential equation models dominate the literature, while recent studies increasingly employ fractional-order models to incorporate memory effects. Deterministic ordinary differential equation models constituted 61.53% (16/26), fractional-order models 23.07% (6/26), stochastic models 7.69% (2/26), linearized models 3.84% (1/26), and hybrid models 3.84% (1/26). Maize streak virus was the most frequently modeled disease, whereas diseases affecting barley and sorghum received comparatively less attention. Common intervention strategies included insecticide application, resistant varieties, quarantine, roguing, and biological control, with integrated approaches generally producing the most effective outcomes. The review also highlights important research gaps, particularly the limited use of field data, climate variables, socioeconomic factors, and forecasting techniques. Overall, this review provides insights into current advances and future research directions in cereal crop disease modeling for sustainable agricultural management.