The　global　food　market＇s　escalating　volatility　has　led　to　a　complex　network　of　uncertainty　and　risk　transmission　across　different　grain　markets.　This　study　utilizes　the　Time-Varying　Parameter　Vector　Autoregression　(TVP-VAR)-Connectedness　approach　to　analyze　the　price　transmission　and　volatility　dynamics　of　key　grains,　including　wheat,　maize,　rice,　barley,　peanut,　soybean,　and　soybean　meal,　and　their　dynamic　spillover　directions,　intensity,　and　network.　By　integrating　the　TVP-VAR-Connectedness　model,　this　research　captures　the　time-varying　variability　and　interconnected　nature　of　global　grain　price　movements.　The　main　findings　reveal　significant　spillover　effects,　particularly　in　corn　prices,　with　prices　of　soybean　dominating　other　grains　while　prices　of　peanut　and　corn　experience　higher　external　spillover　effects　from　other　grains.　The　conclusions　drawn　underscore　the　imperative　for　policymakers　to　consider　a　holistic　perspective　of　all　types　of　grains　when　addressing　global　food　security,　with　this　study　providing　valuable　insights　for　risk　management　in　the　grain　sector　at　both　global　level　and　country　level.