Adverse weather is the primary cause of catastrophic crop losses. In the United States, more than 90% of U.S. crop losses are caused by adverse weather events, including drought, flood, hail, freeze, and windstorm. In China, another of the world’s largest agricultural producers, drought and flood alone account for 80 percent of crop damage.

In the past, estimating the likelihood and magnitude of future crop losses has presented significant challenges. Traditional methods that rely on historical losses have proven unreliable. That’s why AIR developed the first fully probabilistic crop loss models that explicitly capture the effects of weather.

Award Winning Methodology

AIR’s innovative MPCI models—available for the United States and mainland China—represent state-of-the -art tools capable of determining the likelihood of losses arising from all perils covered under the U.S. and Chinese crop insurance programs.

AIR’s MPCI Model for the U.S. is being used by all leading U.S. crop reinsurers and many leading crop insurers—with good reason; employing the same underlying methodology behind the model’s crop yield distributions, AIR repeatedly won the FACTSim (University of Florida's Financial and Agricultural Commodity Trading Simulation) Futures and Options Trading Competition.

Drawing on experience in modeling crop loss in the United States, AIR has developed an MPCI model for China that accounts for the country’s multiple climate zones and regional propensities for different kinds of adverse weather, drought, flood and typhoons. The model accommodates China’s complex cropping practices and policy conditions, including the dependency of policy conditions on the crop development stage.

Agricultural Weather Index Isolates Weather Effect

The AIR MPCI models for the U.S. and China rely on AIR’s Agricultural Weather Index (AWI), a county- and crop-specific index used to assess the overall quality of the growing season. The index takes into account weather variables (precipitation and temperature), soil conditions (available water capacity, surface moisture, and runoff) and crop-specific parameters (like requirements at critical stages of crop growth, planting and harvesting dates, and resiliency to adverse weather). These variables are used in a water balance model that correlates the amount of water available to crops during the growing season with how much water the particular crop requires.

The AIR MPCI model for China covers drought, flood, wind (including the effects from hail and typhoon events), and frost/freeze for all major crops. An additional module covers risk to forestry exposures from fire.

Designed to Meet the Needs of Crop Insurers and Reinsurers

AIR’s Multiple Peril Crop Insurance Model for the United States generates a 10,000-event stochastic catalog of potential future county yield outcomes and associated price scenarios that reflect the most recent volatility in commodity prices. The model captures yield correlations both spatially and between crops to account for widespread weather effects. Once policy conditions are applied to the modeled county yield distributions and modeled prices, portfolio gains and losses can be reported on a pre- or post-Standard Reinsurance Agreement basis and can be aggregated to state, regional and nationwide levels.

The AIR MPCI Model for China is updated regularly to reflect the latest industry information. The model incorporates new crop data and damaged areas from the National Bureau of Statistics of China, new loss and exposure information, and new sets of AWI values. Model updates ensure that analyses reflect the latest available peril information, damaged area and exposure information. The model, which accounts for province-specific insurance programs, allows reinsurers to assess the losses from droughts, floods and typhoons based on company-specific exposure (i.e., total sums insured, crop areas or premiums).

AIR currently offers a multi-peril crop insurance model for:

  • United States
  • Mainland China

AIR Earthquake Model for Australia

U.S. Crop Hail Model
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