This study introduces a novel time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach in order to characterize connectedness of 11 agricultural commodity and Crude Oil futures prices spanning from July 1, 2005 to May 1, 2020. Our results reveal that the system-wide dynamic connectedness is heterogeneous over time and driven by economic events. Peaks have been reached during the Global Financial Crisis, European Governmental Debt Crisis, and the COVID-19 pandemic. Further findings show that commodities such as Crude Oil, Grains, Livestock, Sugar, and Soybean Oil tend to be the main net transmitters of shocks while Corn, Lean Hogs, Soybeans, Cattle, and Wheat are the main receivers of shocks. Pairwise connectedness on the other hand shows that Crude Oil not only affects other commodity markets, but is also equally responsive to innovations that take place in most of these markets explaining the high interconnectedness of the network. Finally, we illustrate the importance of the chosen normalization technique employed in the connectedness framework as the retrieved findings have important implications for investors to design strategies for optimization of portfolio and asset allocation, reduction in downside risk along with hedging strategies. The full implementation and replication code is available at: https://github.com/GabauerDavid/ConnectednessApproach.