Climate-Driven Doubling of U.S. Maize Loss Probability: Interactive Simulation with Neural Network Monte Carlo

Paper: arXiv 2408.02217

Authors: A Samuel Pottinger, Lawson Connor, Brookie Guzder-Williams, Maya Weltman-Fahs, Nick Gondek, Timothy Bowles

Abstract

Climate change not only threatens agricultural producers but also strains related public agencies and financial institutions. These important food system actors include government entities tasked with insuring grower livelihoods and supporting response to continued global warming. We examine future risk within the U.S. Corn Belt geographic region for one such crucial institution: the U.S. Federal Crop Insurance Program. Specifically, we predict the impacts of climate-driven crop loss at a policy-salient “risk unit” scale. Built through our presented neural network Monte Carlo method, simulations anticipate both more frequent and more severe losses that would result in a costly doubling of the annual probability of maize Yield Protection insurance claims at mid-century. We also provide an open source pipeline and interactive visualization tools to explore these results with configurable statistical treatments. Altogether, we fill an important gap in current understanding for climate adaptation by bridging existing historic yield estimation and climate projection to predict crop loss metrics at policy-relevant granularity.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on straightforward statistical formulations for crop loss metrics rather than dense mathematical derivations, making the math relatively accessible. However, it demonstrates exceptional empirical rigor through high-resolution spatial datasets (SCYM and CHC-CMIP6) and rigorous out-of-sample validation. Novelty is strong due to the policy-relevant ‘risk unit’ scale integration with Neural Network Monte Carlo, accompanied by robust open-source tools for reproducibility.

Research Flowchart

  flowchart TD
    A["Research Goal: Predict Climate Change Impacts<br/>on U.S. Crop Insurance Risk"] --> B["Data Inputs: Historical Yields &<br/>Mid-Century Climate Projections"]
    B --> C["Neural Network Monte Carlo<br/>Simulation Pipeline"]
    C --> D["Policy-Salient 'Risk Unit' Scale<br/>Loss Predictions"]
    D --> E["Key Findings: Doubling of Annual Maize<br/>Insurance Claim Probability"]
    D --> F["Outcomes: Open-Source Pipeline &<br/>Interactive Visualization Tools"]