One of the most important consequences of climate change will be its effect upon global agriculture and food supply. In worst case scenarios, increased temperatures and more frequent droughts will create food scarcity and dramatic shifts in the types of crops different regions of the world can grow. But in order to better prepare for these changes, more nuanced forecasts about climate and agriculture are needed. This week, one of the most ambitious projects in this area announced a new phase in creating these important computational tools.
Since 2012, an international group of scientists have worked on the Global Gridded Crop Model Intercomparison (GGCMI) Project, an effort to assess climate impacts on agriculture at continental and global scales and compare and improve existing crop models. The ultimate goal is to create powerful new models that can help decision-makers at the United Nations, the Intergovernmental Panel on Climate Change, and governments around the world manage their food production under a changing climate. But to get to that important final goal, several intermediate steps are required.
Recently, GGCMI published the results of their first phase: a comparison of different crop models each using the same historical weather datasets to “hindcast” agricultural statistics and determine how accurately they “predicted the past.” These trials allowed GGCMI researchers, co-led by CI Fellow and RDCEP research scientist Joshua Elliott, to validate and examine the similarities and differences of the models by comparing how they performed against actual agricultural data.