<![CDATA[Soybean meal is a major protein source in livestock diets, and maintaining soybean quality during storage is critical for feed safety and animal health. However, soybeans are susceptible to quality deterioration due to lipid oxidation, which can be accelerated by unfavorable temperature and humidity conditions and may vary substantially across climatic regions. This study aimed to quantify climate-driven storage risks by simulating the coupled evolution of temperature, moisture content, and peroxide value (PV) in bulk-stored soybean. A one-dimensional radial transient model was implemented in Python to describe heat transfer by conduction, moisture migration by diffusion, and lipid oxidation as temperature- and water activity-dependent processes with Arrhenius kinetics. The climate boundary conditions were derived from the monthly meteorological data for four representative cities: Jakarta (humid tropical), Athens (Mediterranean), Minneapolis (cold temperate), and Lagos (tropical monsoon) over a 180-day storage period starting on October 1. Simulations showed strong thermal inertia in the silo core and climate-dependent gradients near the wall, with warming under tropical climates and cooling under seasonal and cold ones. Moisture variations remained limited in the bulk and were mainly confined to a thin near-wall region owing to slow internal diffusion and boundary equilibration. PV increased monotonically with storage duration and exhibited strong climate dependence, with the highest oxidation risk predicted for warm and humid climates and the lowest for cold climates. Overall, the framework provides a quantitative tool for comparing soybean storage quality risks for animal feed across climatic regimes and supports climate-adapted management strategies to reduce the oxidative deterioration of soybeans.]]>
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