  Climate change not only threatens agricultural producers but also strains
financial institutions. These important food system actors include government
entities tasked with both insuring grower livelihoods and supporting response
to continued global warming. We use an artificial neural network to predict
future maize yields in the U.S. Corn Belt, finding alarming changes to
institutional risk exposure within the Federal Crop Insurance Program.
Specifically, our machine learning method anticipates more frequent and more
severe yield losses that would result in the annual probability of Yield
Protection (YP) claims to more than double at mid-century relative to
simulations without continued climate change. Furthermore, our dual finding of
relatively unchanged average yields paired with decreasing yield stability
reveals targeted opportunities to adjust coverage formulas to include
variability. This important structural shift may help regulators support grower
adaptation to continued climate change by recognizing the value of
risk-reducing strategies such as regenerative agriculture. Altogether, paired
with open source interactive tools for deeper investigation, our risk profile
simulations fill an actionable gap in current understanding, bridging granular
historic yield estimation and climate-informed prediction of future
insurer-relevant loss.
